{ "comparison_id": "0e156cf6", "questions": 20, "questions_file": "logs/freshness-question-pack-2026-06-23.jsonl", "models": [ "ollama", "gpt-4o" ], "unified_grader": true, "grader_model": "gpt-4o", "results": { "ollama_q1": { "id": "fresh-20260623-001", "question": "According to NASA's June 22, 2026 SEWP VI contract release, what are the three acquisition categories, what is the ordering period, and what is the maximum value per contract?", "answer_key": "Categories: Category A IT Solutions; Category B Enterprise-wide IT Service Solutions; Category C IT Mission-Based Services. Ordering period: 10 years, Nov. 1 through Oct. 31, 2036. Maximum value: $20 billion per IDIQ contract.", "source": "https://www.nasa.gov/news-release/nasa-awards-solutions-for-federal-enterprise-procurement-contracts/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I cannot provide an accurate answer regarding NASA's June 22, 2026 SEWP VI contract release based on my current knowledge cutoff date of early 2023. The information requested pertains to a specific fu", "llm_calls": 1, "tool_calls": 0, "tokens_in": 132, "tokens_out": 201, "duration_ms": 2942, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release:\n\n- The three acquisition categories are Category A (IT Solutions), Category B (Enterprise-wide IT Service Solutions), and Category C (IT Mis", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1664, "tokens_out": 163, "duration_ms": 2710, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release:\n\n- The three acquisition categories are:\n - **Category A**: IT Solutions;\n - **Category B**: Enterprise-wide IT Service Solutions;\n - **C", "llm_calls": 4, "tool_calls": 10, "tokens_in": 5582, "tokens_out": 899, "duration_ms": 15057, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are:\n\n- Category A: IT Solutions [1]\n- Category B: Enterprise-wide IT Service Solutions [1]\n- Ca", "llm_calls": 6, "tool_calls": 10, "tokens_in": 4528, "tokens_out": 1219, "duration_ms": 121251, "llm_duration_ms": 121237, "tool_duration_ms": 96928, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer does not provide specific details requested and only states a limitation due to knowledge cutoff date.\nCitation Quality: 2/10 — No citations are provided, and it mentions future events beyond the knowledge cutoff.\nCoverage: 3/10 — Does not address the three acquisition categories, ordering period, or maximum value.\nCalibration: 5/10 — The answer accurately states a limitation but does not provide any specific details requested.\nCognitive Load: 8/10 — Well-structured and easy to follow despite the lack of information.\nOVERALL: 4/10 — Provides no useful information beyond stating limitations.\n\n### B1: LLM + Search\nAccuracy: 2/10 — The answer is not based on the provided key or source, as it cites a non-existent future date (June 22, 2026) and does not provide specific dates for ordering period.\nCitation Quality: 4/10 — Cites an authoritative source but incorrectly states a future event as if it has already happened.\nCoverage: 2/10 — Omits the exact dates of the ordering period.\nCalibration: 5/10 — The answer attempts to provide information but contradicts the provided key and uses incorrect dates.\nCognitive Load: 8/10 — Easy to follow, though the content is misleading.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Provides all requested details accurately (categories, ordering period, maximum value).\nCitation Quality: 7/10 — Correctly cites an authoritative source but does not provide a specific URL.\nCoverage: 8/10 — Addresses multiple perspectives by cross-referencing with the provided key and indicating high confidence.\nCalibration: 9/10 — The answer is confident in its accuracy, which matches the quality of information provided.\nCognitive Load: 7/10 — Slightly cluttered but still easy to follow.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — Provides all requested details accurately (categories, ordering period, maximum value).\nCitation Quality: 9/10 — Correctly cites an authoritative source with a specific URL.\nCoverage: 7/10 — Addresses multiple perspectives by providing citations but does not explicitly mention the three acquisition categories separately in its response.\nCalibration: 8/10 — The answer is confident and accurate, which matches the quality of information provided.\nCognitive Load: 9/10 — Structured well with clear sections for responses and citations.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 2 | 3 | 5 | 8 | 4 |\n| B1 | 2 | 4 | 2 | 5 | 8 | 4 |\n| B2 | 9 | 7 | 8 | 9 | 7 | 8 |\n| MA | 8 | 9 | 7 | 8 | 9 | 8 |\n\n### Summary\n- **B0** and **B1** both have significant accuracy issues due to their reliance on future events and incorrect dates.\n- **B2** is more accurate but slightly less clear in addressing all aspects of the question.\n- **MA** provides the most accurate, well-cited answer with a high overall score.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the SEWP VI contract.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The model correctly states its limitations and low confidence.\nCognitive Load: 5/10 — The answer is clear but lacks useful content.\nOVERALL: 2/10 — The response is clear about its limitations but fails to provide any relevant information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly lists the categories, ordering period, and maximum value.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 5/10 — The answer provides the necessary details but lacks additional perspectives or context.\nCalibration: 9/10 — The confidence level is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A well-structured and accurate response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is fully accurate according to the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 6/10 — The answer covers the main points but lacks broader context or counterarguments.\nCalibration: 10/10 — The confidence level is well-matched to the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — An accurate and well-cited response with excellent calibration.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer is mostly accurate but incorrectly states the ordering period start year.\nCitation Quality: 7/10 — The citation is generic and lacks a direct link or detailed reference.\nCoverage: 6/10 — The answer provides the necessary details but lacks additional perspectives or context.\nCalibration: 8/10 — The confidence is high, but the caveat about verification suggests some uncertainty.\nCognitive Load: 8/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — A mostly accurate response with minor citation and calibration issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1: LLM + Search | 9 | 8 | 5 | 9 | 9 | 8 |\n| B2: LLM + Search + Reflection | 10 | 9 | 6 | 10 | 9 | 9 |\n| MA: Multi-Agent | 9 | 7 | 6 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 3, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 2, "Citation Quality": 4, "Coverage": 2, "Calibration": 5, "Cognitive Load": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 7 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 9 }, "Comparison Table": {}, "Summary": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 6, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q2": { "id": "fresh-20260623-002", "question": "For NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, when is the launch window, how many participants/teams are involved, and approximately how many experiments will the rocket carry?", "answer_key": "Launch window: Wednesday, June 24, 2026, 5:30–9:30 a.m. EDT, with backup Thursday, June 25. Nearly 250 participants from 38 university/community college teams; nearly 50 experiments.", "source": "https://www.nasa.gov/centers-and-facilities/wallops/nasa-sounding-rocket-to-launch-student-experiments/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I do not have access to real-time databases or specific upcoming mission details for NASA's RockSatX/RockOn combined sounding rocket mission scheduled for June 2026. The exact launch window, number of", "llm_calls": 1, "tool_calls": 0, "tokens_in": 134, "tokens_out": 294, "duration_ms": 4358, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: medium\n\n- Launch window: The launch window for NASA's June 2026 RockSatX/RockOn combined sounding rocket mission is between 5:30 and 9:30 a.m. EDT on Wednesday, June 24, with a backup day ", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1637, "tokens_out": 250, "duration_ms": 3865, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: low\n\n- Launch window: Between 5:30 and 9:30 a.m. EDT on June 24, with a backup day on June 25 [SOURCE 1]. NASA’s official source does not specify the exact time window, so this is an estim", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5479, "tokens_out": 1097, "duration_ms": 17992, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nFor NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, the launch window is between 5:30 and 9:30 a.m. EDT on either Wednesday, June 24 or Thursday, June 25 [1]. Nearly 250 ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4669, "tokens_out": 1299, "duration_ms": 79017, "llm_duration_ms": 78995, "tool_duration_ms": 46362, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — Provides some relevant information but lacks specific dates, numbers of teams and experiments.\nCitation Quality: 3/10 — No citations provided; mentions sources without links or references.\nCoverage: 4/10 — Omitting the exact launch window, number of participants, and number of experiments.\nCalibration: 5/10 — Low confidence expressed despite providing general information.\nCognitive Load: 8/10 — Well-structured but lacks specific details needed for accuracy.\n\nOVERALL: 5/10 — Provides some relevant information but is incomplete and lacking in detail.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Accurate claims supported by citations.\nCitation Quality: 8/10 — Reliable sources provided, though one citation is not linked here.\nCoverage: 7/10 — Covers launch window, number of participants/teams, and experiments but mentions ambiguity regarding the exact experiment count.\nCalibration: 9/10 — Appropriate confidence level given that information comes from a reliable source.\nCognitive Load: 8/10 — Structured well with clear points.\n\nOVERALL: 8/10 — Reliable and accurate while providing specific details.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Accurate claims supported by citations, though slight ambiguity is noted.\nCitation Quality: 8/10 — Reliable sources provided, though one citation is not linked here.\nCoverage: 7/10 — Covers launch window, number of participants/teams, and experiments but mentions estimation for some details.\nCalibration: 9/10 — Appropriate confidence level given that information comes from a reliable source with some inferred data.\nCognitive Load: 8/10 — Structured well with clear points.\n\nOVERALL: 8/10 — Reliable and accurate while providing specific details, though slight ambiguity is noted.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Accurate claims supported by citations.\nCitation Quality: 9/10 — Reliable sources provided with a link to the source URL.\nCoverage: 9/10 — Covers launch window, number of participants/teams, and experiments comprehensively.\nCalibration: 9/10 — Appropriate confidence level given that information comes from an official NASA source.\nCognitive Load: 8/10 — Structured well with clear points.\n\nOVERALL: 9/10 — Most accurate and comprehensive while providing specific details.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|--------|\n| B0 | 5 | 3 | 4 | 5 | 8 | 5 |\n| B1 | 8 | 8 | 7 | 9 | 8 | 8 |\n| B2 | 8 | 8 | 7 | 9 | 8 | 8 |\n| MA | 9 | 9 | 9 | 9 | 8 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not provide any specific details about the 2026 mission.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — The answer only discusses past missions and does not address the 2026 mission.\nCalibration: 3/10 — The answer correctly states low confidence but fails to provide relevant information.\nCognitive Load: 5/10 — The answer is clear but lacks relevant content.\nOVERALL: 2/10 — The answer fails to address the question with specific details or citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately provides the launch window, participant numbers, and experiment count.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though only one source is mentioned.\nCoverage: 7/10 — The answer covers the main points but lacks discussion of potential changes or uncertainties.\nCalibration: 8/10 — The confidence level is appropriately set to medium given the accurate information.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — A strong answer with accurate details and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer provides accurate details about the mission.\nCitation Quality: 9/10 — The citation is real and directly supports the claims.\nCoverage: 8/10 — The answer includes a note on confidence and potential variability, adding depth.\nCalibration: 7/10 — The confidence is set to low, which is slightly conservative given the accurate information.\nCognitive Load: 8/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — A comprehensive answer with accurate information and thoughtful coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer provides all the correct details as per the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though the URL is not fully specified.\nCoverage: 9/10 — The answer includes caveats about potential changes, adding depth.\nCalibration: 9/10 — The confidence is set to high, which is appropriate given the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — An excellent answer with comprehensive coverage and accurate details.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 1 | 3 | 5 | 2 |\n| B1 | 9 | 8 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 7 | 8 | 8 |\n| MA | 10 | 9 | 9 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 3, "Coverage": 4, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 1, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q3": { "id": "fresh-20260623-003", "question": "In NASA's June 22, 2026 media advisory, which country is scheduled to sign the Artemis Accords, at what time/date, who will host, and what signer number will it become?", "answer_key": "Botswana; 9:30 a.m. EDT Thursday, June 25, 2026; hosted by NASA Deputy Administrator Matt Anderson; Botswana will be the 68th country to sign.", "source": "https://www.nasa.gov/news-release/nasa-invites-media-to-botswana-artemis-accords-signing-ceremony/", "answers": { "B0: Vanilla LLM": { "answer_preview": "Based on the information available in public records as of my last update, I do not have specific details regarding a NASA media advisory from June 22, 2026, concerning the signing of the Artemis Acco", "llm_calls": 1, "tool_calls": 0, "tokens_in": 135, "tokens_out": 339, "duration_ms": 4986, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In NASA's June 22, 2026 media advisory, Botswana is scheduled to sign the Artemis Accords at 9:30 a.m. EDT on Thursday, June 25. The event will be hosted by NASA Deputy Administrator Matt Anderson. Th", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1611, "tokens_out": 207, "duration_ms": 3825, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "### Revised Answer\n\nDate/Time: June 25, 2026 at 9:30 a.m. EDT [N]\nCountry: The Republic of Botswana [N]\nHost: NASA Deputy Administrator Matt Anderson [N]\nSigner Number: Not explicitly confirmed [N]\n\n*", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5465, "tokens_out": 1192, "duration_ms": 45836, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn NASA's June 22, 2026 media advisory, Botswana is scheduled to sign the Artemis Accords at 9:30 a.m. EDT on Thursday, June 25, at NASA Headquarters in Washington. This signing will be hos", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4777, "tokens_out": 1253, "duration_ms": 89360, "llm_duration_ms": 89330, "tool_duration_ms": 48446, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not provide the specific date, time, country, host, and signer number as requested. It also contradicts the key by mentioning a non-specific media advisory.\nCitation Quality: 2/10 — No citations are provided, and the source is not referenced or verified.\nCoverage: 1/10 — The answer does not cover multiple perspectives or counterarguments; it lacks detailed information and is largely speculative.\nCalibration: 2/10 — The stated confidence level of low is inappropriate given that this information could be verified if current, but the lack of a specific source suggests uncertainty.\nCognitive Load: 7/10 — The answer is generally understandable, though it could be more structured.\n\nOVERALL: 3/10\n\n### B1: LLM + Search\nAccuracy: 8/10 — Provides correct information about Botswana signing the Artemis Accords but omits specifying that this event was on June 25, 2026.\nCitation Quality: 9/10 — Provides a direct URL to NASA's press release as a source.\nCoverage: 4/10 — Addresses multiple perspectives and includes a high-confidence statement without contradicting the key. However, it could provide more detailed information or counterarguments.\nCalibration: 8/10 — The stated confidence level of high is appropriate given that this information is from an official NASA source.\nCognitive Load: 7/10 — The answer is clear and concise but lacks structure.\n\nOVERALL: 7/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — Provides the correct date, time, country, host, and notes that the signer number was not explicitly confirmed.\nCitation Quality: 9/10 — Includes a detailed citation from NASA’s press release.\nCoverage: 6/10 — The answer reflects on the provided evidence but does not explore other potential signatories or counterarguments in detail.\nCalibration: 8/10 — The stated confidence level of moderate is appropriate given that some information was inferred rather than explicitly stated.\nCognitive Load: 7/10 — The response is well-structured and includes explanations.\n\nOVERALL: 7/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Provides the correct date, time, country, host, and correctly identifies Botswana as the 68th signer.\nCitation Quality: 9/10 — Includes a specific URL to NASA’s press release as a citation.\nCoverage: 5/10 — The answer does not explore other perspectives or counterarguments but is straightforward in its response.\nCalibration: 9/10 — The stated high confidence level matches the accuracy of the information provided, which comes directly from an official source.\nCognitive Load: 7/10 — The structure is clear and easy to follow.\n\nOVERALL: 8/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|--------|\n| B0 | 3/10 | 2/10 | 1/10 | 2/10 | 7/10 | 3/10 |\n| B1 | 8/10 | 9/10 | 4/10 | 8/10 | 7/10 | 7/10 |\n| B2 | 8/10 | 9/10 | 6/10 | 8/10 | 7/10 | 7/10 |\n| MA | 9/10 | 9/10 | 5/10 | 9/10 | 7/10 | 8/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not provide any specific details about the event in question.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer provides some background on the Artemis Accords but lacks specific details about the event.\nCalibration: 4/10 — The low confidence is appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth and specificity.\nOVERALL: 3/10 — The answer is mostly generic and lacks the necessary details to address the question.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies all details as per the authoritative answer key.\nCitation Quality: 9/10 — The citation is real and relevant, directly supporting the claims.\nCoverage: 9/10 — The answer covers all aspects of the question comprehensively.\nCalibration: 10/10 — The high confidence is justified by the accurate and well-supported information.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — A well-rounded and accurate response with strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer is mostly correct but incorrectly states that the signer number is not confirmed.\nCitation Quality: 8/10 — The citation is relevant and supports most claims, but there is a minor discrepancy regarding the signer number.\nCoverage: 8/10 — The answer addresses most aspects but incorrectly doubts the signer number.\nCalibration: 7/10 — The moderate confidence is slightly under-calibrated given the mostly accurate information.\nCognitive Load: 8/10 — The answer is well-structured but slightly overcomplicates the signer number issue.\nOVERALL: 8/10 — A strong answer with minor inaccuracies regarding the signer number.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies all details as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a complete URL or reference.\nCoverage: 9/10 — The answer covers all aspects of the question comprehensively.\nCalibration: 10/10 — The high confidence is justified by the accurate and well-supported information.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — A well-rounded and accurate response with minor citation formatting issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 4 | 5 | 3 |\n| B1 | 10 | 9 | 9 | 10 | 9 | 9 |\n| B2 | 8 | 8 | 8 | 7 | 8 | 8 |\n| MA | 10 | 8 | 9 | 10 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 1, "Calibration": 2, "Cognitive Load": 7, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 4, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 6, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 5, "Calibration": 9, "Cognitive Load": 7, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q4": { "id": "fresh-20260623-004", "question": "According to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, which Webb instrument was used, what unusual chemical measurements were highlighted, and where/when was the paper published?", "answer_key": "Instrument: NIRSpec / Near-Infrared Spectrograph. Measurements: carbon and deuterium/heavy-hydrogen chemical ratios unlike solar-system comets. Paper published June 22 in Nature.", "source": "https://science.nasa.gov/missions/webb/nasas-webb-finds-clues-to-ancient-distant-origin-of-comet-3i-atlas/", "answers": { "B0: Vanilla LLM": { "answer_preview": "To accurately answer this question, I would need specific details from NASA's June 22, 2026 press release about the James Webb Space Telescope (Webb) observations of comet 3I/ATLAS and the correspondi", "llm_calls": 1, "tool_calls": 0, "tokens_in": 137, "tokens_out": 427, "duration_ms": 11731, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to the provided evidence:\n\n- **Webb instrument used**: The NASA James Webb Space Telescope's NIRSpec (Near-Infrared Spectrograph) was used [SOURCE 1].\n\n- **Unusual chemical measurements high", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1773, "tokens_out": 268, "duration_ms": 4787, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to NASA's June 22, 2026 story on comet 3I/ATLAS [SOURCE 1], the Webb instrument used was the NIRSpec (Near-Infrared Spectrograph) [N]. The unusual chemical measurements highlighted included ", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5999, "tokens_out": 1030, "duration_ms": 17095, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, the NIRSpec (Near-Infrared Spectrograph) instrument was used. Unusual chemical measurements highlighted included specific che", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4685, "tokens_out": 1502, "duration_ms": 116067, "llm_duration_ms": 116049, "tool_duration_ms": 92072, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The response accurately mentions that specific data was not available as of October 2023, but it doesn't provide the correct information from the key answer.\nCitation Quality: 4/10 — No citations are provided; the response is based on current knowledge cut-off date.\nCoverage: 5/10 — The response does not address multiple perspectives or counterarguments regarding the observation of comet 3I/ATLAS by the Webb telescope.\nCalibration: 6/10 — The response acknowledges a lack of specific data but expresses confidence, which is somewhat calibrated to the actual quality of the information available.\nCognitive Load: 8/10 — The response is well-structured and easy to follow, though it could be more concise.\n\nOverall: 6/10\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer closely matches the key facts provided in the authoritative source.\nCitation Quality: 8/10 — The citation is relevant and directly supports the claims, though it could be more explicit about the date of access.\nCoverage: 7/10 — The response addresses the instrument used, unusual chemical measurements, and where/when the paper was published but does not cover multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence level is high because the information aligns with the provided source.\nCognitive Load: 8/10 — The response is clear and well-structured.\n\nOverall: 8/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer closely matches the key facts provided in the authoritative source.\nCitation Quality: 8/10 — The citation is relevant and directly supports the claims, though it could be more explicit about the date of access.\nCoverage: 7/10 — The response addresses the instrument used, unusual chemical measurements, and where/when the paper was published but does not cover multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence level is high because the information aligns with the provided source.\nCognitive Load: 8/10 — The response is clear and well-structured.\n\nOverall: 8/10\n\n### MA: Multi-Agent\nAccuracy: 6/10 — The response contains a factual error in stating that the information was from June 22, 2026. It also uses an assumed source for part of its answer.\nCitation Quality: 5/10 — One citation is provided but incorrectly dated and based on a hypothetical scenario.\nCoverage: 4/10 — The response does not address multiple perspectives or counterarguments regarding the observation of comet 3I/ATLAS by the Webb telescope.\nCalibration: 6/10 — The confidence level is high, but it lacks actual evidence to support its claims.\nCognitive Load: 7/10 — The response is clear and well-structured.\n\nOverall: 5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 6/10 | 4/10 | 5/10 | 6/10 | 8/10 | 6/10 |\n| B1 | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 8/10 |\n| B2 | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 8/10 |\n| MA | 6/10 | 5/10 | 4/10 | 6/10 | 7/10 | 5/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any correct information about the June 22, 2026 Webb story.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — The answer acknowledges the lack of information but does not explore multiple perspectives or counterarguments.\nCalibration: 2/10 — The answer correctly states low confidence, but the lack of any correct information justifies an even lower score.\nCognitive Load: 5/10 — The answer is clear and easy to follow but lacks substance.\nOVERALL: 2/10 — The response fails to provide any relevant or accurate information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims made.\nCoverage: 8/10 — The answer covers the main points but does not explore counterarguments or additional perspectives.\nCalibration: 9/10 — The stated high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims made.\nCoverage: 8/10 — The answer covers the main points but does not explore counterarguments or additional perspectives.\nCalibration: 9/10 — The stated high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is assumed but aligns with the authoritative source.\nCoverage: 8/10 — The answer covers the main points but does not explore counterarguments or additional perspectives.\nCalibration: 9/10 — The stated high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with strong citation support.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 8 | 9 | 9 | 9 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 4, "Coverage": 5, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 6, "Citation Quality": 5, "Coverage": 4, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q5": { "id": "fresh-20260623-005", "question": "In NASA's June 22, 2026 Chandra image article, where is the possible supernova remnant located and what would make it notable if confirmed?", "answer_key": "It is in the middle/central region of the Milky Way. If confirmed, it would be one of the closest supernova remnants ever discovered to the supermassive black hole at the Galactic Center.", "source": "https://www.nasa.gov/image-article/nasas-chandra-finds-possible-supernova-remnant/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I do not have direct access to NASA's specific June 22, 2026 Chandra image article as it hasn't been released yet. Therefore, I can provide a general context and explanation based on typical character", "llm_calls": 1, "tool_calls": 0, "tokens_in": 126, "tokens_out": 431, "duration_ms": 8880, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: medium\n\nAccording to the NASA article [1], the possible supernova remnant found by Chandra is located in an intriguing neighborhood in the middle of our galaxy. This location would be nota", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1640, "tokens_out": 199, "duration_ms": 17345, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: medium\n\nThe possible supernova remnant mentioned in NASA's June 22, 2026 Chandra image article [SOURCE 1] is located in an intriguing neighborhood in the middle of our galaxy. While the ex", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5557, "tokens_out": 968, "duration_ms": 16055, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe possible supernova remnant is located in the middle of our galaxy. This location makes it notable if confirmed, as it would be one of the closest ever discovered to the supermassive bla", "llm_calls": 6, "tool_calls": 8, "tokens_in": 5533, "tokens_out": 1278, "duration_ms": 112773, "llm_duration_ms": 112758, "tool_duration_ms": 91744, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer provides a general context but contradicts the key by suggesting the exact location cannot be determined due to the article not being released yet. It also fails to mention the central region or proximity to the supermassive black hole.\nCitation Quality: 3/10 — No citations are provided, and the explanation is based on general knowledge rather than specific information.\nCoverage: 4/10 — Addresses some aspects of SNRs but does not cover multiple perspectives or counterarguments.\nCalibration: 5/10 — The confidence level is low due to a lack of specific information, which aligns with the factual issues.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow, though it could be more concise.\n\nOVERALL: 4/10\n\n### B1: LLM + Search\nAccuracy: 7/10 — The answer correctly identifies the location in the middle of the Milky Way and notes the potential notability due to proximity to the supermassive black hole. However, it doesn't provide specific coordinates or details.\nCitation Quality: 9/10 — Provides a real, relevant citation from NASA’s official article.\nCoverage: 6/10 — Addresses location but lacks in-depth discussion of SNRs and their confirmation processes.\nCalibration: 7/10 — The confidence level is medium, which aligns with the factual basis provided.\nCognitive Load: 8/10 — Easy to follow and concise.\n\nOVERALL: 7/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 6/10 — Similar to B1, it correctly identifies the location but doesn’t provide specific details. The answer lacks depth in explaining SNRs.\nCitation Quality: 9/10 — Provides a real and relevant citation from NASA’s official article.\nCoverage: 5/10 — Focuses on location and notability without addressing multiple perspectives or counterarguments.\nCalibration: 6/10 — Confidence is medium, which aligns with the provided information but could be higher given the accuracy issues.\nCognitive Load: 8/10 — The answer is well-structured but could benefit from more specific details.\n\nOVERALL: 5/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately states that the possible supernova remnant is in the middle of the Milky Way and notes its potential notability.\nCitation Quality: 8/10 — Provides a relevant citation from NASA’s official article. However, it doesn’t include the exact date (June 22, 2026) or the specific source URL.\nCoverage: 7/10 — Addresses location and notability but could provide more details on confirmation processes.\nCalibration: 9/10 — The confidence level is high based on evidence from NASA’s Chandra X-ray Observatory, which aligns well with the accuracy provided.\nCognitive Load: 8/10 — Easy to follow and concise.\n\nOVERALL: 8/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|---------|-------------|----------------|---------|\n| B0 | 6 | 3 | 4 | 5 | 8 | 4 |\n| B1 | 7 | 9 | 6 | 7 | 8 | 7 |\n| B2 | 6 | 9 | 5 | 6 | 8 | 5 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |\n\nThe Multi-Agent approach (MA) received the highest overall score due to its accurate and well-supported claims, relevant citations, and high confidence level. The Vanilla LLM provided a general context but lacked specific details and accuracy in its claims.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not address the specific question about the June 22, 2026 Chandra image article.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 3/10 — Provides general information about supernova remnants but lacks specific details related to the question.\nCalibration: 2/10 — The answer correctly states low confidence, but the lack of specific information is a significant issue.\nCognitive Load: 5/10 — The answer is structured and easy to follow but lacks relevance to the question.\nOVERALL: 2/10 — The response is mostly irrelevant to the specific question asked.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 9/10 — Provides a relevant and real citation that supports the claims.\nCoverage: 7/10 — Addresses the main points but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The medium confidence level is appropriate given the accurate information provided.\nCognitive Load: 8/10 — The answer is concise and easy to understand.\nOVERALL: 8/10 — A well-supported and accurate response with minor room for improvement in coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Accurately describes the location and significance of the supernova remnant.\nCitation Quality: 8/10 — Provides a relevant citation but lacks additional sources or details.\nCoverage: 8/10 — Offers insights into the implications of the discovery but could expand on counterarguments.\nCalibration: 8/10 — The medium confidence level is suitable given the accurate information.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — A comprehensive and accurate response with slight room for improvement in citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 8/10 — Provides a relevant citation but lacks additional sources.\nCoverage: 7/10 — Covers the main points but could include more perspectives or counterarguments.\nCalibration: 9/10 — The high confidence level is justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — A solid and accurate response with minor areas for improvement in coverage and citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 2 | 5 | 2 |\n| B1 | 9 | 9 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 8 | 8 | 8 | 8 | 8 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 4, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 9, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 6, "Citation Quality": 9, "Coverage": 5, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q6": { "id": "fresh-20260623-006", "question": "For NASA's US Spacewalk 95 announcement, what task will astronauts perform, when is the spacewalk scheduled to begin, and who are the three preview briefing participants listed?", "answer_key": "Task: replace a wrist joint on the ISS Canadarm2 robotic arm. Start: approximately 8:35 a.m. EDT Tuesday, June 30, 2026. Briefing participants: Bill Spetch, Fiona Antkowiak, and Jason Dyer.", "source": "https://www.nasa.gov/news-release/nasa-to-cover-us-spacewalk-95-host-preview-news-conference/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I do not have access to real-time data or specific announcements from NASA regarding upcoming spacewalks as of my last update in 2023. To accurately provide the details about US Spacewa", "llm_calls": 1, "tool_calls": 0, "tokens_in": 126, "tokens_out": 230, "duration_ms": 4010, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- Task for astronauts during US Spacewalk 95: Replace a wrist joint on the Canadarm2 robotic arm.\n- Scheduled start time of the spacewalk: Approximately 8:35 a.m. EDT on Tuesday, June 30.\n- Preview br", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1487, "tokens_out": 181, "duration_ms": 6218, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "### Revised Answer:\n- Task for astronauts during US Spacewalk 95: replace a wrist joint on the Canadarm2 robotic arm [SOURCE 3].\n- Scheduled start time of the spacewalk: June 30, 2026 (time and date a", "llm_calls": 4, "tool_calls": 8, "tokens_in": 7894, "tokens_out": 957, "duration_ms": 17488, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nFor NASA's US Spacewalk 95 announcement, astronauts will perform the task of replacing a wrist joint on the Canadarm2 robotic arm. The spacewalk is scheduled to begin at approximately 8:35 ", "llm_calls": 14, "tool_calls": 8, "tokens_in": 26772, "tokens_out": 3369, "duration_ms": 178646, "llm_duration_ms": 178619, "tool_duration_ms": 91294, "converged": true, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 8/10 — The answer correctly identifies that real-time data is not available, but it does not provide the specific details from the source.\nCitation Quality: 2/10 — There are no citations provided to support the claims.\nCoverage: 4/10 — The answer omits providing the exact task, start time, and names of participants as per the source.\nCalibration: 6/10 — While the answer suggests a lack of real-time data access, it does not explicitly state the specific details are from an unverified source.\nCognitive Load: 8/10 — The response is clear but could be more structured by directly addressing the question.\n\nOVERALL: 5/10 — The answer acknowledges limitations and provides a general framework but lacks detailed information and citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly identifies the task, start time, and participants as per the source.\nCitation Quality: 8/10 — The citation provided is relevant but not explicitly linked or included in the text.\nCoverage: 7/10 — The answer addresses multiple perspectives by providing the task, date, and participants.\nCalibration: 9/10 — The answer provides a high level of confidence based on available information.\nCognitive Load: 8/10 — The response is clear but could be more structured with direct quotes from the citation.\n\nOVERALL: 8/10 — The answer is well-supported by citations and directly addresses the question, though it lacks some structure.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the task, start time (approximate), and participants as per the source.\nCitation Quality: 7/10 — While the sources are relevant, they are not explicitly linked in the text.\nCoverage: 8/10 — The answer addresses multiple perspectives by providing details on the task, date, and participants.\nCalibration: 9/10 — The answer provides a medium level of confidence that is appropriately matched to the available information.\nCognitive Load: 7/10 — The response could be clearer with direct quotes from the citation.\n\nOVERALL: 8/10 — The answer is detailed but could benefit from more explicit citations and structure.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer correctly identifies the task, start time (approximate), and participants as per the source.\nCitation Quality: 3/10 — Only a general citation is provided without specific URLs or details.\nCoverage: 6/10 — The answer addresses multiple perspectives by providing the task, date, and participants but omits additional participants from the preview briefing.\nCalibration: 7/10 — The answer provides a medium level of confidence which aligns with the available information.\nCognitive Load: 5/10 — The response could be clearer and more structured.\n\nOVERALL: 6/10 — The answer is detailed but lacks explicit citations and structure, leading to some confusion.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 8/10 | 2/10 | 4/10 | 6/10 | 8/10 | 5/10 |\n| B1 | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 8/10 |\n| B2 | 9/10 | 7/10 | 8/10 | 9/10 | 7/10 | 8/10 |\n| MA | 9/10 | 3/10 | 6/10 | 7/10 | 5/10 | 6/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any specific information about US Spacewalk 95.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — The answer mentions general sources but does not address the specific question.\nCalibration: 2/10 — The answer correctly states low confidence, but it provides no useful information.\nCognitive Load: 5/10 — The answer is clear but lacks substance and relevance.\nOVERALL: 1/10 — The response fails to address the question with any specific or useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately provides the task, timing, and participants.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but only one source is mentioned.\nCoverage: 9/10 — The answer covers all aspects of the question comprehensively.\nCalibration: 10/10 — The confidence level is appropriately high given the accuracy of the information.\nCognitive Load: 10/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A well-rounded and accurate response with minor citation limitations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The task and participants are correct, but the timing is not fully confirmed.\nCitation Quality: 7/10 — Citations are relevant but lack explicit confirmation of the timing.\nCoverage: 8/10 — The answer addresses the main points but lacks full confirmation of the timing.\nCalibration: 7/10 — The medium confidence is appropriate given the partial uncertainty.\nCognitive Load: 9/10 — The answer is clear and well-structured, though slightly hedged.\nOVERALL: 8/10 — A mostly accurate response with some uncertainty in timing.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The task, timing, and participants are correctly identified.\nCitation Quality: 6/10 — The citation is vague and lacks specificity.\nCoverage: 8/10 — The answer covers the main points but notes some gaps.\nCalibration: 8/10 — The medium confidence is reasonable given the minor gaps.\nCognitive Load: 9/10 — The answer is clear and concise.\nOVERALL: 8/10 — A strong response with minor citation and coverage issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 1 |\n| B1 | 10 | 8 | 9 | 10 | 10 | 9 |\n| B2 | 8 | 7 | 8 | 7 | 9 | 8 |\n| MA | 9 | 6 | 8 | 8 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 2, "Coverage": 4, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 7, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 3, "Coverage": 6, "Calibration": 7, "Cognitive Load": 5, "OVERALL": 6 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 10, "Cognitive Load": 10, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 8, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 } } }, "ollama_q7": { "id": "fresh-20260623-007", "question": "What real-world data-collection bottleneck does the June 22, 2026 arXiv paper 'AutoDex' claim to address, and what loop must run without human intervention?", "answer_key": "It addresses scalable real-world dexterous grasping data collection: teleoperation is slow/operator-biased and simulation cannot certify contact validity. The loop is perception, execution, labeling, and reset running without human intervention.", "source": "https://arxiv.org/abs/2606.23689v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The June 22, 2026 arXiv paper titled \"AutoDex\" claims to address the bottleneck in real-world data collection by automating the process of data organization and labeling. Specifically, it aims to stre", "llm_calls": 1, "tool_calls": 0, "tokens_in": 130, "tokens_out": 302, "duration_ms": 4510, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The June 22, 2026 arXiv paper 'AutoDex' claims to address the real-world data-collection bottleneck in obtaining large-scale physical outcomes of grasp attempts for dexterous grasping [N]. Specificall", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1701, "tokens_out": 206, "duration_ms": 4014, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The June 22, 2026 arXiv paper 'AutoDex' claims to address the real-world data-collection bottleneck of obtaining large-scale valid physical outcomes for grasp attempts [SOURCE 1]. Specifically, it men", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5824, "tokens_out": 1165, "duration_ms": 32470, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAutoDex addresses the real-world data-collection bottleneck of obtaining valid physical outcomes at scale for dexterous grasping [1]. Specifically, it automates the entire collection loop w", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4667, "tokens_out": 1318, "duration_ms": 58786, "llm_duration_ms": 58778, "tool_duration_ms": 18168, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 4/10 — The answer omits key points about the specific bottleneck and the loop described in the authoritative source. It also introduces a hypothetical paper that does not exist as stated.\nCitation Quality: 2/10 — Cites an unverified date and title, lacks direct citation from the correct source.\nCoverage: 3/10 — Addresses some elements but misses critical details like the specific bottleneck (scalable real-world dexterous grasping data collection) and the full loop description.\nCalibration: 3/10 — Low confidence justified by factual inaccuracies and lack of verification.\nCognitive Load: 7/10 — The answer is somewhat clear, but it introduces fictional elements which may confuse readers.\n\nOVERALL: 4/10 — The response is poorly calibrated due to inaccuracies and lacks relevant detail.\n\n### B1: LLM + Search\nAccuracy: 6/10 — Addresses the bottleneck correctly but omits specific details about the loop. The confidence level is high, which is not justified by the provided information.\nCitation Quality: 3/10 — Mentions a future paper without providing direct citation or reliable source.\nCoverage: 4/10 — Begins to address the core issues but fails to provide full context and specifics of the loop.\nCalibration: 5/10 — The confidence level is too high given the lack of actual evidence.\nCognitive Load: 6/10 — Suggests a future paper, which might be confusing for readers who are looking for current information.\n\nOVERALL: 5/10 — Provides some relevant information but is overly optimistic about its reliability and completeness.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — Correctly identifies the bottleneck and provides a detailed description of the loop. However, there are minor inaccuracies in the citation details.\nCitation Quality: 7/10 — Provides inline citations from the correct source but uses an outdated reference.\nCoverage: 9/10 — Extensively addresses multiple perspectives and counterarguments by discussing the challenges and proposed solution thoroughly.\nCalibration: 8/10 — Appropriate confidence level given that it relies on a specific, cited paper.\nCognitive Load: 7/10 — Well-structured but includes some technical jargon which might be challenging for non-experts.\n\nOVERALL: 8/10 — A high-quality response with accurate information and appropriate framing.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Provides an accurate description of the bottleneck and the loop, aligning closely with the authoritative source.\nCitation Quality: 9/10 — Cites a relevant paper from 2023 instead of the correct one but accurately references it.\nCoverage: 8/10 — Addresses multiple aspects but does not explicitly mention counterarguments or alternatives.\nCalibration: 7/10 — Slightly overconfident about the operational details, as some specifics are omitted.\nCognitive Load: 6/10 — Concise and clear, though it introduces a caveat that some details may require further review.\n\nOVERALL: 8/10 — A solid response with high accuracy and good calibration but could benefit from including more context or potential counterarguments.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 4/10 | 2/10 | 3/10 | 3/10 | 7/10 | 4/10 |\n| B1 | 6/10 | 3/10 | 4/10 | 5/10 | 6/10 | 5/10 |\n| B2 | 8/10 | 7/10 | 9/10 | 8/10 | 7/10 | 8/10 |\n| MA | 9/10 | 9/10 | 8/10 | 7/10 | 6/10 | 8/10 |\n\nThe Multi-Agent approach (MA) and the Authoritative Answer Key are the most accurate, well-calibrated, and comprehensive. The B2 approach is nearly as good but slightly less confident in its citations. The B1 and B0 approaches show promise but fall short due to inaccuracies and overconfidence.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer incorrectly describes the paper's focus and claims.\nCitation Quality: 0/10 — No real citations are provided, and the paper is acknowledged as non-existent.\nCoverage: 1/10 — The answer lacks multiple perspectives and counterarguments.\nCalibration: 2/10 — The confidence is low, which matches the poor quality of the answer.\nCognitive Load: 4/10 — The answer is somewhat clear but misleading due to incorrect information.\nOVERALL: 2/10 — The answer is largely inaccurate and lacks proper citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the bottleneck and the loop that must run without human intervention.\nCitation Quality: 0/10 — No real citations are provided to support the claims.\nCoverage: 5/10 — The answer addresses the main point but lacks depth and counterarguments.\nCalibration: 7/10 — The confidence is high, which is mostly justified by the accuracy of the content.\nCognitive Load: 7/10 — The answer is clear and easy to follow.\nOVERALL: 5/10 — The answer is accurate but lacks citations and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately describes the bottleneck and the loop.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though the source number is generic.\nCoverage: 7/10 — The answer covers the main points well but could include more perspectives.\nCalibration: 8/10 — The medium confidence is appropriate given the quality of the answer.\nCognitive Load: 8/10 — The answer is well-structured and easy to understand.\nOVERALL: 8/10 — A well-rounded answer with good accuracy and citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the bottleneck and the loop.\nCitation Quality: 9/10 — The citation is real and directly supports the claims.\nCoverage: 8/10 — The answer covers the main points and acknowledges gaps in detail.\nCalibration: 9/10 — The high confidence is justified by the quality of the answer.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — An excellent answer with strong accuracy, citations, and clarity.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 1 | 2 | 4 | 2 |\n| B1 | 8 | 0 | 5 | 7 | 7 | 5 |\n| B2 | 9 | 8 | 7 | 8 | 8 | 8 |\n| MA | 9 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 4, "Citation Quality": 2, "Coverage": 3, "Calibration": 3, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 4, "Calibration": 5, "Cognitive Load": 6, "OVERALL": 5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 9, "Calibration": 8, "Cognitive Load": 7, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 7, "Cognitive Load": 6, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 4, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 0, "Coverage": 5, "Calibration": 7, "Cognitive Load": 7, "OVERALL": 5 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q8": { "id": "fresh-20260623-008", "question": "In 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' what three components are combined in the proposed training method?", "answer_key": "YaRN-based positional extrapolation, randomized positional encoding, and a length curriculum.", "source": "https://arxiv.org/abs/2606.23687v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "In the paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning\" by researchers from Carnegie Mellon University and Google Research, a novel training method combining three com", "llm_calls": 1, "tool_calls": 0, "tokens_in": 119, "tokens_out": 342, "duration_ms": 5117, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Based on the provided evidence [SOURCE 1], in 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' three components are combined in the proposed training method:\n\n1. YaRN-based", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1646, "tokens_out": 129, "duration_ms": 4825, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Confidence: medium\n\nBased on the information provided in the paper titled \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning,\" the three components combined in the proposed tra", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5439, "tokens_out": 789, "duration_ms": 22158, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' the three components combined in the proposed training method are:\n\n1. YaRN-based positional extrapolation.\n2", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4041, "tokens_out": 800, "duration_ms": 29514, "llm_duration_ms": 29505, "tool_duration_ms": 5780, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer correctly identifies YaRN-based positional extrapolation, but incorrectly includes \"randomized context masking\" and \"long-context sampling,\" which are not in the authoritative answer key.\nCitation Quality: 3/10 — No specific citations provided, only general references to a knowledge base.\nCoverage: 5/10 — The answer addresses multiple aspects of training methods but does not cover all required components accurately.\nCalibration: 6/10 — Stated confidence is high, but the accuracy and coverage suggest it should be lower.\nCognitive Load: 8/10 — Well-structured with clear points but could benefit from more precise language.\n\nOverall: 6/10 — The answer contains some useful information but is not accurate or well-supported by citations.\n\n### B1: LLM + Search\nAccuracy: 7/10 — Correctly identifies the three components.\nCitation Quality: 5/10 — Provides a specific citation, which is relevant but not directly from the authoritative source.\nCoverage: 8/10 — Accurately covers all required components and addresses multiple aspects of the method.\nCalibration: 7/10 — Stated confidence aligns with the accuracy since the citation supports the claims.\nCognitive Load: 9/10 — Very concise and easy to follow.\n\nOverall: 7/10 — A good answer that is accurate, well-supported, and clear.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — Correctly identifies all three components without extraneous details.\nCitation Quality: 6/10 — Provides a specific citation with the correct reference number, which is relevant.\nCoverage: 9/10 — Accurately covers all required components and directly justifies each one from the source text.\nCalibration: 8/10 — Stated confidence aligns well with the accuracy and coverage of the answer.\nCognitive Load: 9/10 — Well-structured and easy to follow.\n\nOverall: 8/10 — A high-quality answer that is accurate, well-supported, and clear.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — Correctly identifies all three components without extraneous details.\nCitation Quality: 6/10 — Provides a specific citation with the correct reference number, which is relevant but lacks explicit detail on the source within it.\nCoverage: 9/10 — Accurately covers all required components and directly justifies each one from the source text.\nCalibration: 8/10 — Stated confidence aligns well with the accuracy and coverage of the answer.\nCognitive Load: 9/10 — Well-structured and easy to follow.\n\nOverall: 8/10 — A high-quality answer that is accurate, well-supported, and clear.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 7 | 3 | 5 | 6 | 8 | 6 |\n| B1 | 7 | 5 | 8 | 7 | 9 | 7 |\n| B2 | 8 | 6 | 9 | 8 | 9 | 8 |\n| MA | 8 | 6 | 9 | 8 | 9 | 8 |\n\nThe Multi-Agent (MA) approach and the B2 approach are the highest in overall quality, with accurate, well-supported answers that are easy to follow. The B1 approach is also strong but slightly less detailed in coverage. The Vanilla LLM answer is the least effective due to inaccuracies and lack of proper citations.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not accurately list the components from the authoritative answer key.\nCitation Quality: 0/10 — No real citations are provided, and the reference to the paper is vague.\nCoverage: 2/10 — The answer provides some context but fails to address multiple perspectives or counterarguments.\nCalibration: 2/10 — The confidence is high despite inaccuracies in the content.\nCognitive Load: 5/10 — The structure is clear, but the content is misleading.\nOVERALL: 2/10 — The answer is largely inaccurate and lacks proper citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The components listed match the authoritative answer key.\nCitation Quality: 3/10 — The citation is vague and lacks specificity.\nCoverage: 5/10 — The answer is concise but does not explore different perspectives or counterarguments.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 8/10 — The answer is clear and easy to follow.\nOVERALL: 7/10 — Accurate and clear but lacks detailed citations and coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The components listed match the authoritative answer key.\nCitation Quality: 5/10 — The citation is more specific but still lacks direct links or detailed references.\nCoverage: 6/10 — Provides a justification but lacks exploration of counterarguments.\nCalibration: 8/10 — The confidence is slightly lower than B1, which is appropriate given the citation quality.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 7/10 — Accurate with some citation improvement, but still lacks depth in coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The components listed match the authoritative answer key.\nCitation Quality: 6/10 — Provides a more specific citation but still lacks direct links.\nCoverage: 6/10 — Includes a caveat but does not explore counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 8/10 — The answer is clear and easy to follow.\nOVERALL: 8/10 — Accurate with slightly better citation quality, but still lacks depth in coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 2 | 5 | 2 |\n| B1 | 10 | 3 | 5 | 9 | 8 | 7 |\n| B2 | 10 | 5 | 6 | 8 | 8 | 7 |\n| MA | 10 | 6 | 6 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 3, "Coverage": 5, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 5, "Coverage": 8, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 3, "Coverage": 5, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 6, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q9": { "id": "fresh-20260623-009", "question": "What stop-and-go simplification does 'CoorDex' criticize, and what control formulation does it introduce?", "answer_key": "It criticizes walking to an object, stopping to manipulate it, then resuming locomotion, often with low-DoF open-close end effectors. It introduces coordinated latent residual control for high-DoF dexterous loco-manipulation on the move.", "source": "https://arxiv.org/abs/2606.23680v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Based on the information available in the literature, CoorDex criticizes the stop-and-go simplification in the context of control theory and adaptive systems, particularly focusing on how traditional ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 113, "tokens_out": 268, "duration_ms": 4017, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "CoorDex criticizes the stop-and-go simplification in humanoid loco-manipulation, where the process is often broken down into walking to an object, stopping to manipulate it, and then resuming locomoti", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1643, "tokens_out": 253, "duration_ms": 6717, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "CoorDex addresses a specific challenge in humanoid loco-manipulation where actions are often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming l", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5518, "tokens_out": 907, "duration_ms": 29703, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nCoorDex criticizes the stop-and-go simplification in humanoid loco-manipulation, where robots walk to an object, stop to manipulate it, and then resume locomotion. CoorDex introduces a coor", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4027, "tokens_out": 1024, "duration_ms": 42903, "llm_duration_ms": 42894, "tool_duration_ms": 2550, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer is generally accurate but deviates from the key by focusing on control theory more than necessary, and the explanation of CoorDex's approach lacks specific details.\nCitation Quality: 6/10 — One relevant citation provided, but it does not directly support the claims about CoorDex.\nCoverage: 5/10 — Does not address multiple perspectives or counterarguments.\nCalibration: 7/10 — The answer provides a general overview and acknowledges that specific details are not widely documented.\nCognitive Load: 8/10 — Well-structured, but the explanation could be clearer.\n\nOVERALL: 6/10\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer is highly accurate as it closely follows the key information and includes a direct citation from the authoritative source.\nCitation Quality: 9/10 — Directly cites the relevant paper, which provides strong support for the claims.\nCoverage: 7/10 — Addresses the critique but does not explore counterarguments or alternative perspectives.\nCalibration: 8/10 — The answer is confident and consistent with its content quality.\nCognitive Load: 9/10 — Clear and concise.\n\nOVERALL: 8.5/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — The answer accurately identifies the stop-and-go process but omits details about CoorDex’s approach, such as its latent residual control formulation.\nCitation Quality: 6/10 — Only one citation provided, and it is not directly relevant to the claims made.\nCoverage: 5/10 — Does not address multiple perspectives or counterarguments.\nCalibration: 7/10 — The answer is confident but lacks specific details about CoorDex’s method.\nCognitive Load: 8/10 — Clear, but the explanation could be more structured.\n\nOVERALL: 6.5/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Accurately identifies the stop-and-go process and mentions CoorDex's coordinated latent residual control formulation.\nCitation Quality: 7/10 — The citation is relevant but not directly from the authoritative source.\nCoverage: 6/10 — Does not address multiple perspectives or counterarguments, and does not fully explain CoorDex’s approach.\nCalibration: 8/10 — High confidence in the answer despite some gaps.\nCognitive Load: 9/10 — Clear and concise.\n\nOVERALL: 7.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|-------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 7 | 6 | 5 | 7 | 8 | 6 |\n| B1 | 9 | 9 | 7 | 8 | 9 | 8.5 |\n| B2 | 7 | 6 | 5 | 7 | 8 | 6.5 |\n| MA | 9 | 7 | 6 | 8 | 9 | 7.5 |\n\nThe B1 approach stands out for its high accuracy, relevant citations, and clear structure, making it the most well-rounded answer.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately describe the stop-and-go simplification or the control formulation introduced by CoorDex.\nCitation Quality: 1/10 — The citation is unrelated to CoorDex and does not support the claims made.\nCoverage: 2/10 — The answer lacks multiple perspectives and counterarguments, focusing instead on unrelated control theory concepts.\nCalibration: 3/10 — The confidence level is medium, but the content is largely inaccurate and unrelated.\nCognitive Load: 4/10 — The structure is somewhat clear, but the content is misleading and not relevant to the question.\nOVERALL: 2/10 — The answer is largely inaccurate and unsupported by relevant citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the stop-and-go simplification and the control formulation introduced by CoorDex.\nCitation Quality: 9/10 — The citation is real, relevant, and directly supports the claims made.\nCoverage: 8/10 — The answer addresses the main points but lacks exploration of counterarguments or additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to follow.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer perfectly aligns with the authoritative answer key.\nCitation Quality: 10/10 — The citation is accurate, relevant, and directly supports the claims.\nCoverage: 9/10 — The answer thoroughly addresses the main points but could include more on counterarguments.\nCalibration: 9/10 — The medium confidence is slightly conservative given the accuracy and support of the content.\nCognitive Load: 10/10 — The answer is exceptionally clear and well-structured.\nOVERALL: 10/10 — The answer is comprehensive, accurate, and well-supported.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer is mostly accurate but lacks some detail on the control formulation.\nCitation Quality: 2/10 — The citation is not real and does not support the claims.\nCoverage: 7/10 — The answer covers the main points but lacks depth and counterarguments.\nCalibration: 7/10 — The high confidence is not fully justified due to the lack of a real citation.\nCognitive Load: 8/10 — The answer is clear and easy to follow but lacks depth.\nOVERALL: 6/10 — The answer is mostly accurate but lacks proper citation and depth.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 1 | 2 | 3 | 4 | 2 |\n| B1 | 9 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 9 | 9 | 10 | 10 |\n| MA | 8 | 2 | 7 | 7 | 8 | 6 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 5, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8.5 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 5, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6.5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 7.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 2, "Calibration": 3, "Cognitive Load": 4, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 10, "OVERALL": 10 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 2, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6 } } }, "ollama_q10": { "id": "fresh-20260623-010", "question": "What problem with modern text-to-image models motivates 'Semantic Browsing,' and what user capability does the method aim to provide?", "answer_key": "Strict prompt adherence can collapse samples into a single visual interpretation, reducing meaningful diversity. Semantic Browsing aims to let users navigate controlled, structured diversity through meaningful design choices.", "source": "https://arxiv.org/abs/2606.23679v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The primary motivation for Semantic Browsing in text-to-image models stems from the difficulty users face when specifying complex visual concepts and ensuring that these are accurately captured by the", "llm_calls": 1, "tool_calls": 0, "tokens_in": 116, "tokens_out": 365, "duration_ms": 5554, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Problem with modern text-to-image models motivating 'Semantic Browsing': Modern text-to-image models excel in visual fidelity and prompt adherence but tend to produce outputs that are less diverse, of", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1485, "tokens_out": 192, "duration_ms": 3175, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**Problem with modern text-to-image models motivates 'Semantic Browsing':**\nModern text-to-image models excel in visual fidelity and prompt adherence but often generate samples that collapse into a si", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5032, "tokens_out": 973, "duration_ms": 24179, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nSemantic Browsing addresses the issue of reduced diversity in modern text-to-image models, where these models tend to generate outputs that collapse into a single visual interpretation due ", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4072, "tokens_out": 1050, "duration_ms": 45445, "llm_duration_ms": 45434, "tool_duration_ms": 4612, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 7/10 — The answer correctly identifies the problem and the user capability but does not fully cover multiple perspectives or counterarguments. It also omits some details present in the authoritative answer key.\n\nCitation Quality: 8/10 — The citations provided are relevant, although one is an outdated reference (Dosovityski et al., 2021 instead of Dosovitskiy et al., 2021).\n\nCoverage: 6/10 — It addresses the problem and user capability but misses some nuances. It does not explicitly mention counterarguments or alternative viewpoints.\n\nCalibration: 7/10 — The confidence is generally well-calibrated, given that it acknowledges limitations like the use of outdated references.\n\nCognitive Load: 8/10 — The answer is well-structured and easy to follow but could be improved with clearer explanations.\n\nOverall: 7/10 — A solid answer that captures most aspects correctly but lacks in depth and nuance.\n\n---\n\n### B1: LLM + Search\n\nAccuracy: 8/10 — The answer accurately identifies the problem and user capability. It references the correct source for support, although the citation is not directly provided.\n\nCitation Quality: 7/10 — The source reference is implied but not explicitly stated, which could be a weakness in rigorous evaluation.\n\nCoverage: 6/10 — Like B0, it misses some nuances and does not address counterarguments or alternative viewpoints.\n\nCalibration: 8/10 — The confidence is well-calibrated given the references used.\n\nCognitive Load: 9/10 — The answer is concise but could be more structured for better readability.\n\nOverall: 7/10 — A good attempt that accurately captures the essence with minor issues in citations and structure.\n\n---\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 8/10 — The answer accurately identifies the problem and user capability, closely aligning with the key points. It references the correct source for support but uses a different citation style.\n\nCitation Quality: 8/10 — The citations are relevant and properly formatted, enhancing credibility.\n\nCoverage: 7/10 — It addresses the problem and user capability effectively but could benefit from explicitly mentioning counterarguments or alternative viewpoints.\n\nCalibration: 9/10 — The confidence is well-calibrated given the direct use of evidence.\n\nCognitive Load: 8/10 — The answer is structured well but could be more detailed for clarity.\n\nOverall: 8/10 — A refined approach that captures key points accurately with a minor focus on nuance and counterarguments.\n\n---\n\n### MA: Multi-Agent\n\nAccuracy: 7/10 — Similar to the others, it identifies the problem and user capability. However, it uses an outdated reference (Xu et al., 2023 instead of Xu et al., 2023).\n\nCitation Quality: 6/10 — The citation is correct but using a single source limits the credibility.\n\nCoverage: 5/10 — It misses some nuances and does not address counterarguments or alternative viewpoints effectively.\n\nCalibration: 7/10 — The confidence is somewhat justified by the referenced source, though it acknowledges limitations.\n\nCognitive Load: 8/10 — The answer is well-structured but could be clearer with more detail.\n\nOverall: 6/10 — An attempt that captures most aspects correctly but lacks depth and nuance.\n\n---\n\n### Comparison Table\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 7 | 8 | 6 | 7 | 8 | 7 |\n| B1 | 8 | 7 | 6 | 8 | 9 | 7 |\n| B2 | 8 | 8 | 7 | 9 | 8 | 8 |\n| MA | 7 | 6 | 5 | 7 | 8 | 6 |\n\nEach approach has its strengths and weaknesses, with B2 providing the most comprehensive answer.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 4/10 — The answer misinterprets the problem as being about language sensitivity rather than diversity collapse.\nCitation Quality: 3/10 — The citation is incorrect and does not support the claims made.\nCoverage: 4/10 — The answer touches on user capabilities but misses the core issue of diversity collapse.\nCalibration: 3/10 — The confidence is overstated given the inaccuracies and citation issues.\nCognitive Load: 5/10 — The structure is clear, but the content is misleading and not well-supported.\nOVERALL: 4/10 — The answer is poorly aligned with the authoritative answer key and lacks accurate citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the problem of diversity collapse and the user capability provided.\nCitation Quality: 9/10 — The citation is accurate and directly supports the claims.\nCoverage: 7/10 — The answer addresses the main points but lacks depth in exploring counterarguments.\nCalibration: 8/10 — The confidence level is appropriate given the accuracy and citation quality.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A well-supported and accurate answer with strong citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately describes the problem and user capability.\nCitation Quality: 9/10 — The citations are accurate and directly support the claims.\nCoverage: 8/10 — The answer covers the main points and provides a good level of detail.\nCalibration: 9/10 — The confidence is well-calibrated with the quality of the answer.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — A comprehensive and well-supported answer that aligns closely with the authoritative answer key.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the problem and user capability but lacks some detail.\nCitation Quality: 8/10 — The citation is relevant but not the exact authoritative source.\nCoverage: 7/10 — The answer covers the main points but could explore more perspectives.\nCalibration: 7/10 — The confidence is slightly overstated given the limited source.\nCognitive Load: 8/10 — The answer is clear and well-organized.\nOVERALL: 8/10 — A solid answer with good citation quality, though it could benefit from additional sources.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 4 | 3 | 4 | 3 | 5 | 4 |\n| B1 | 8 | 9 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 8 | 8 | 7 | 7 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 8, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 5, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 4, "Citation Quality": 3, "Coverage": 4, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 } } }, "ollama_q11": { "id": "fresh-20260623-011", "question": "According to the AIR arXiv abstract, what limitation of prior interleaved-reasoning/tool-use work does AIR target?", "answer_key": "Prior work focuses mainly on predefined heuristic visual manipulations for vision-perception tasks and is inherently unable to address numerical computation problems; AIR targets adaptive interleaved reasoning with code in MLLMs.", "source": "https://arxiv.org/abs/2606.23678v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Based on the abstract from the AIR (Artificial Intelligence in Robotics) arXiv paper, the work specifically targets a limitation of prior interleaved-reasoning/tool-use approaches by focusing on impro", "llm_calls": 1, "tool_calls": 0, "tokens_in": 115, "tokens_out": 263, "duration_ms": 3946, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to the AIR arXiv abstract, the limitation of prior interleaved-reasoning/tool-use work that AIR targets is that existing approaches typically rely on predefined heuristics for visual manipul", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1664, "tokens_out": 176, "duration_ms": 4417, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to the AIR arXiv abstract [1], the limitation of prior interleaved-reasoning/tool-use work that AIR targets is its reliance on predefined heuristics for visual manipulation.\n\nConfidence: med", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5698, "tokens_out": 942, "duration_ms": 26674, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to the AIR arXiv abstract, one limitation of prior interleaved-reasoning/tool-use work that AIR targets is their reliance on predefined heuristics for visual manipulation and inab", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3734, "tokens_out": 815, "duration_ms": 34679, "llm_duration_ms": 34667, "tool_duration_ms": 2157, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 8/10 — The answer correctly identifies that prior work relies on predefined heuristics for visual manipulation but incorrectly adds that these methods are inherently incapable of addressing numerical computation problems, which is not a direct quote from the key source.\n\nCitation Quality: 7/10 — Provides relevant citations, but the reference to Mordatch & Abbeel (2015) is not directly related to the specific limitation mentioned in the AIR abstract.\n\nCoverage: 6/10 — The answer does not address counterarguments or multiple perspectives on the limitations of prior work.\n\nCalibration: 7/10 — The high confidence level is justified by the accurate citation, but the inclusion of numerical computation issues seems exaggerated based on the key source.\n\nCognitive Load: 8/10 — Well-structured and easy to follow, though it could be more concise.\n\nOverall: 7/10\n\n### B1: LLM + Search\n\nAccuracy: 9/10 — The answer accurately identifies that prior work relies on predefined heuristics for visual manipulation and omits the part about numerical computation problems, which is not in the key source but still aligns with the abstract.\n\nCitation Quality: 8/10 — Provides a relevant citation directly from the AIR arXiv paper.\n\nCoverage: 6/10 — Does not address multiple perspectives or counterarguments on the limitations of prior work.\n\nCalibration: 9/10 — The high confidence level is well-justified by the direct quote and specific citation.\n\nCognitive Load: 7/10 — Clear but could benefit from summarization.\n\nOverall: 8/10\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 6/10 — The answer correctly identifies that prior work relies on predefined heuristics for visual manipulation, but it fails to mention the inability to address numerical computation problems due to their exclusive focus on visual operations, which is directly stated in the key source.\n\nCitation Quality: 7/10 — Provides a relevant citation from the AIR arXiv paper, though it does not explicitly state that this work targets numerical computation limitations.\n\nCoverage: 5/10 — The answer focuses narrowly and misses addressing other potential limitations or perspectives on the work.\n\nCalibration: 6/10 — The medium confidence level is justified but could be higher given the clear statement in the abstract.\n\nCognitive Load: 8/10 — Clear structure with a field missing note, making it easy to follow but leaving room for improvement.\n\nOverall: 6.5/10\n\n### MA: Multi-Agent\n\nAccuracy: 7/10 — The answer correctly identifies that prior work relies on predefined heuristics for visual manipulation and mentions the inability to address numerical computation problems due to their exclusive focus on visual operations, aligning with the key source.\n\nCitation Quality: 9/10 — Provides relevant citations directly from the AIR arXiv paper.\n\nCoverage: 6/10 — Does not address multiple perspectives or counterarguments on the limitations of prior work.\n\nCalibration: 8/10 — The high confidence level is justified by the accurate and detailed citation, though it could be more calibrated to the exact phrasing in the key source.\n\nCognitive Load: 9/10 — Well-structured with clear caveats and field missing notes, making it easy to follow.\n\nOverall: 8.5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 7/10 | 7/10 | 6/10 | 7/10 | 8/10 | 7/10 |\n| B1 | 9/10 | 8/10 | 6/10 | 9/10 | 7/10 | 8/10 |\n| B2 | 6/10 | 7/10 | 5/10 | 6/10 | 8/10 | 6.5/10 |\n| MA | 8/10 | 9/10 | 6/10 | 8/10 | 9/10 | 8.5/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer incorrectly focuses on robotics and tool use, which is not mentioned in the authoritative answer key.\nCitation Quality: 2/10 — The citation is fabricated and does not support the claims made.\nCoverage: 2/10 — The answer does not address the key limitation of numerical computation problems.\nCalibration: 2/10 — The confidence is high despite significant inaccuracies.\nCognitive Load: 4/10 — The structure is clear, but the content is misleading and incorrect.\nOVERALL: 3/10 — The answer is largely inaccurate and poorly supported by citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the limitations as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though it lacks detail.\nCoverage: 8/10 — The answer covers the main limitation but does not explore counterarguments.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy of the answer.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A well-rounded and accurate response with good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — The answer partially identifies the limitation but misses the numerical computation aspect.\nCitation Quality: 7/10 — The citation is relevant but does not fully support the claims made.\nCoverage: 6/10 — The answer lacks depth in addressing the full scope of limitations.\nCalibration: 6/10 — The confidence is medium, which is somewhat appropriate given the partial accuracy.\nCognitive Load: 8/10 — The answer is clear and structured, though incomplete.\nOVERALL: 7/10 — A reasonably accurate answer but lacking in full coverage of the limitations.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the limitations as per the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims.\nCoverage: 9/10 — The answer addresses the main limitation and acknowledges the lack of detailed explanation.\nCalibration: 10/10 — The confidence level is well-matched to the accuracy of the answer.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — An accurate and well-supported answer with excellent calibration.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 2 | 2 | 2 | 4 | 3 |\n| B1 | 9 | 8 | 8 | 9 | 9 | 9 |\n| B2 | 7 | 7 | 6 | 6 | 8 | 7 |\n| MA | 10 | 9 | 9 | 10 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 9, "Cognitive Load": 7, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 6, "Citation Quality": 7, "Coverage": 5, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6.5 }, "MA: Multi-Agent": { "Accuracy": 7, "Citation Quality": 9, "Coverage": 6, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 2, "Calibration": 2, "Cognitive Load": 4, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 7, "Coverage": 6, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q12": { "id": "fresh-20260623-012", "question": "What open theoretical gap does 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' identify, and which optimizers does it contrast with AdamW?", "answer_key": "It identifies the lack of rigorous convergence theory for AdamW under heavy-tailed stochastic gradient noise in LLM pretraining. It contrasts AdamW with sign-based optimizers such as Lion and Muon, and with AdaGrad.", "source": "https://arxiv.org/abs/2606.23676v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The open theoretical gap identified by the title \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" concerns the effectiveness and robustness of AdamW (a variant of the Adam optimizer) in sc", "llm_calls": 1, "tool_calls": 0, "tokens_in": 122, "tokens_out": 724, "duration_ms": 10726, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Open theoretical gap identified: The open problem addresses whether AdamW can converge under heavy-tailed noise regimes, given its second-moment accumulator may create an obstruction [N].\n\nOptimizers ", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1747, "tokens_out": 239, "duration_ms": 3798, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The open theoretical gap identified in \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" concerns whether AdamW can converge under heavy-tailed stochastic gradient noise conditions, as oppo", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5796, "tokens_out": 925, "duration_ms": 42569, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe open theoretical gap that 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' identifies is whether AdamW can converge under heavy-tailed stochastic gradient noise, contrasting", "llm_calls": 10, "tool_calls": 9, "tokens_in": 9432, "tokens_out": 2133, "duration_ms": 152583, "llm_duration_ms": 152573, "tool_duration_ms": 106412, "converged": true, "iterations": 2 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 8/10 — Provides relevant information but contradicts the answer key by omitting the specific mention of \"LLM pretraining.\"\nCitation Quality: 7/10 — Includes multiple citations, but one citation is irrelevant (Reddi et al., 2018).\nCoverage: 6/10 — Addresses the open theoretical gap and contrasts with other optimizers but lacks detailed discussion on heavy-tailed noise.\nCalibration: 7/10 — The answer acknowledges the need for more research, which matches the quality of the information provided.\nCognitive Load: 9/10 — Structured and well-organized.\n\nOverall: 7/10\n\n### B1: LLM + Search\n\nAccuracy: 6/10 — Contradicts by omitting \"LLM pretraining\" as a specific context. Also, the citation [N] is not provided.\nCitation Quality: 4/10 — Only one real citation and it’s not relevant to the open problem discussed.\nCoverage: 5/10 — Does not provide enough detail on heavy-tailed noise or AdamW specifically.\nCalibration: 6/10 — High confidence despite contradictions in the answer.\nCognitive Load: 7/10 — Short and straightforward.\n\nOverall: 5/10\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 8/10 — Addresses key points correctly but misrepresents the importance of heavy-tailed noise in the context of AdamW's traditional performance.\nCitation Quality: 7/10 — Provides relevant citations, though one citation is not directly related to the open problem discussed.\nCoverage: 6/10 — Addresses multiple perspectives by mentioning Lion, Muon, and AdaGrad but lacks depth on heavy-tailed noise.\nCalibration: 7/10 — High confidence despite some misrepresentations in the context of the answer key.\nCognitive Load: 8/10 — Well-structured with a balanced discussion.\n\nOverall: 7/10\n\n### MA: Multi-Agent\n\nAccuracy: 6/10 — Contradicts by not addressing LLM pretraining as a specific context. Also, the citation provided is unrelated to the open problem.\nCitation Quality: 4/10 — Irrelevant citation used and one citation missing.\nCoverage: 5/10 — Discusses heavy-tailed noise but does so in a vague manner without explicit connection to AdamW’s performance.\nCalibration: 6/10 — The medium confidence is appropriate given the lack of specific details about the open problem.\nCognitive Load: 7/10 — Structured with clear sections.\n\nOverall: 5/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 8 | 7 | 6 | 7 | 9 | 7 |\n| B1 | 6 | 4 | 5 | 6 | 7 | 5 |\n| B2 | 8 | 7 | 6 | 7 | 8 | 7 |\n| MA | 6 | 4 | 5 | 6 | 7 | 5 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer correctly identifies the issue of heavy-tailed noise but fails to mention the specific optimizers contrasted with AdamW.\nCitation Quality: 4/10 — Citations are real but do not directly support the claims about the open problem or the optimizers contrasted.\nCoverage: 5/10 — The answer discusses heavy-tailed noise but lacks depth in contrasting AdamW with the specific optimizers mentioned in the authoritative answer.\nCalibration: 6/10 — The confidence level is medium, which is appropriate given the partial accuracy.\nCognitive Load: 7/10 — The answer is generally well-structured and easy to follow.\nOVERALL: 5/10 — The answer provides a basic understanding but lacks specific details and accurate citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — Correctly identifies the open problem and the optimizers contrasted with AdamW.\nCitation Quality: 7/10 — The citation is relevant and supports the claims, though the date appears incorrect.\nCoverage: 7/10 — Covers the main aspects of the open problem and contrasts with other optimizers.\nCalibration: 8/10 — The high confidence is justified by the accurate identification of the problem and optimizers.\nCognitive Load: 8/10 — The answer is concise and easy to understand.\nOVERALL: 8/10 — A well-rounded answer with accurate information and relevant citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Accurately identifies the open problem and the optimizers contrasted with AdamW.\nCitation Quality: 9/10 — Provides a relevant and accurate citation that supports the claims.\nCoverage: 9/10 — Thoroughly covers the open problem and contrasts with other optimizers, including additional insights.\nCalibration: 9/10 — High confidence is appropriate given the comprehensive and accurate response.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — An excellent answer with comprehensive coverage and accurate citations.\n\n### MA: Multi-Agent\nAccuracy: 7/10 — Correctly identifies the open problem but lacks depth in discussing the optimizers contrasted with AdamW.\nCitation Quality: 5/10 — The citation provided is not directly relevant to the open problem discussed.\nCoverage: 6/10 — Covers the main issue but lacks depth in contrasting with specific optimizers.\nCalibration: 6/10 — Medium confidence is somewhat justified, but the lack of depth affects the overall quality.\nCognitive Load: 7/10 — The answer is generally clear but could be more detailed.\nOVERALL: 6/10 — A decent answer but lacks depth and relevant citations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 4 | 5 | 6 | 7 | 5 |\n| B1 | 8 | 7 | 7 | 8 | 8 | 8 |\n| B2 | 9 | 9 | 9 | 9 | 9 | 9 |\n| MA | 7 | 5 | 6 | 6 | 7 | 6 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 7 }, "B1: LLM + Search": { "Accuracy": 6, "Citation Quality": 4, "Coverage": 5, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 6, "Citation Quality": 4, "Coverage": 5, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 4, "Coverage": 5, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 7, "Citation Quality": 5, "Coverage": 6, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 6 } } }, "ollama_q13": { "id": "fresh-20260623-013", "question": "What limitation in existing mental-health assessment approaches does 'PsyBridge' claim to address?", "answer_key": "Existing approaches rely on isolated screening instruments or data-driven models, lack interpretability and multi-dimensional integration, and focus on individual indicators like depression or anxiety rather than comprehensive explainable decision support.", "source": "https://arxiv.org/abs/2606.23673v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Based on available information, \"PsyBridge\" appears to be an approach that addresses the limitation of traditional mental health assessment tools in capturing a comprehensive understanding of patients", "llm_calls": 1, "tool_calls": 0, "tokens_in": 107, "tokens_out": 473, "duration_ms": 6984, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: medium\n\nPsyBridge claims to address the limitation of existing mental-health assessment approaches that rely on isolated screening instruments or data-driven models which often lack interp", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1481, "tokens_out": 98, "duration_ms": 1720, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**Answer Revised:**\n\nConfidence: medium\n\nAccording to the paper \"PsyBridge: A Hybrid Intelligent Framework for Multi-Dimensional Mental Health Assessment and Decision Support,\" existing mental-health ", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4950, "tokens_out": 783, "duration_ms": 13255, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nPsyBridge claims to address the limitation that existing mental-health assessment approaches rely on isolated screening instruments or data-driven models, which often lack interpretability ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3547, "tokens_out": 684, "duration_ms": 91653, "llm_duration_ms": 91639, "tool_duration_ms": 80537, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 8/10 — The answer provides a good overview of existing limitations and correctly identifies them. However, it does not fully capture all aspects mentioned in the authoritative answer key.\nCitation Quality: 6/10 — Includes relevant citations but some information is derived from assumptions rather than direct quotes.\nCoverage: 7/10 — Addresses multiple perspectives (social desirability bias, lack of insight, communication barriers) and counterarguments are hinted at.\nCalibration: 7/10 — The confidence level matches the quality of the answer, which has both accurate and less precise parts.\nCognitive Load: 8/10 — Well-structured with clear sections but could benefit from tighter integration of sources.\n\nOverall: 7/10 — Addresses key points effectively but lacks precision in some areas.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly identifies the main limitation and provides a relevant source.\nCitation Quality: 8/10 — Directly quotes from the provided source, supporting the claim accurately.\nCoverage: 6/10 — Fails to mention other limitations like social desirability bias or communication barriers.\nCalibration: 7/10 — Confidence is medium, which aligns with the quality of the answer as it relies on a single source.\nCognitive Load: 9/10 — Concise and clear, though could be slightly more detailed.\n\nOverall: 8/10 — Strong but misses some critical details in favor of brevity.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — The answer accurately quotes the source but does not fully cover all aspects mentioned in the authoritative answer key.\nCitation Quality: 9/10 — Direct quote from a relevant source supports the claim effectively.\nCoverage: 8/10 — Addresses multiple perspectives and counterarguments, though some details are implicit.\nCalibration: 7/10 — Confidence is medium, which aligns with the quality of the answer as it uses one main source.\nCognitive Load: 8/10 — Well-structured with clear sections.\n\nOverall: 7.5/10 — Addresses key points effectively but could be more detailed and explicit.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer correctly identifies the main limitation and provides a relevant source.\nCitation Quality: 8/10 — Direct quote from the provided source supports the claim accurately.\nCoverage: 6/10 — Fails to mention other limitations like social desirability bias or communication barriers.\nCalibration: 7/10 — Confidence is high, but this aligns with the quality of the answer as it relies on a single source and misses some details.\nCognitive Load: 9/10 — Concise and clear.\n\nOverall: 8/10 — Strong but could be more comprehensive.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 7.5 | 6 | 7 | 7 | 8 | 7 |\n| B1 | 9 | 8 | 6 | 7 | 9 | 8 |\n| B2 | 7.5 | 9 | 8 | 7 | 8 | 7.5 |\n| MA | 9 | 8 | 6 | 7 | 9 | 8 |\n\nEach approach has its strengths and weaknesses, with B1 and MA providing the strongest answers due to their direct sourcing from a relevant paper. However, all could be improved by including more details on additional limitations such as social desirability bias or communication barriers.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not accurately reflect the specific limitations PsyBridge addresses, as outlined in the authoritative answer key.\nCitation Quality: 2/10 — Citations are not relevant to PsyBridge and do not support the claims made.\nCoverage: 4/10 — The answer discusses some general limitations of mental health assessments but misses the specific focus of PsyBridge.\nCalibration: 3/10 — The confidence level is medium, but the answer lacks alignment with the authoritative source.\nCognitive Load: 5/10 — The answer is somewhat structured but includes irrelevant information, making it harder to follow.\nOVERALL: 3/10 — The answer fails to accurately address the question and lacks relevant citations.\n\n### B1: LLM + Search\nAccuracy: 5/10 — The answer partially aligns with the authoritative answer key but lacks detail.\nCitation Quality: 1/10 — The citation is missing, making it impossible to verify the claims.\nCoverage: 3/10 — The answer briefly mentions the limitation but lacks depth and multiple perspectives.\nCalibration: 4/10 — The confidence level is medium, but the lack of citation undermines the reliability.\nCognitive Load: 4/10 — The answer is concise but lacks sufficient detail and context.\nOVERALL: 3/10 — The answer is incomplete and lacks proper citation, reducing its credibility.\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer accurately reflects the limitations PsyBridge addresses, as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and directly supports the claims made.\nCoverage: 7/10 — The answer covers the main limitation but could explore more perspectives.\nCalibration: 7/10 — The confidence level is medium, which is appropriate given the reliance on a single source.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and well-cited, though it could benefit from additional perspectives.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer closely aligns with the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims effectively.\nCoverage: 8/10 — The answer addresses the main limitation and acknowledges potential gaps.\nCalibration: 9/10 — The high confidence level is justified by the strong alignment with the authoritative source.\nCognitive Load: 9/10 — The answer is clear, concise, and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and effectively communicates the key points.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 2 | 4 | 3 | 5 | 3 |\n| B1 | 5 | 1 | 3 | 4 | 4 | 3 |\n| B2 | 8 | 8 | 7 | 7 | 8 | 8 |\n| MA | 9 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 9, "Coverage": 8, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7.5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 4, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 5, "Citation Quality": 1, "Coverage": 3, "Calibration": 4, "Cognitive Load": 4, "OVERALL": 3 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 8, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q14": { "id": "fresh-20260623-014", "question": "In the June 22, 2026 arXiv paper on bit manipulation puzzles, what is the task objective and what LLM failure mode do the authors say traditional methods induce?", "answer_key": "Objective: discover a hidden logical rule transforming input binary strings to outputs, then apply it to unseen inputs. Traditional methods force LLMs to simulate complex boolean logic/arithmetic, leading to hallucinations.", "source": "https://arxiv.org/abs/2606.23672v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I apologize, but as of my current knowledge cutoff in 2023, there isn't a specific arXiv paper published on June 22, 2026, that addresses bit manipulation puzzles with the task objective and LLM failu", "llm_calls": 1, "tool_calls": 0, "tokens_in": 130, "tokens_out": 253, "duration_ms": 3779, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Task objective: Discover a hidden logical rule transforming input binary strings to outputs, then apply it to unseen inputs [SOURCE 1].\n\nLLM failure mode induced by traditional methods: Traditional me", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1711, "tokens_out": 225, "duration_ms": 3512, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "Task objective: Discover a hidden logical rule transforming input binary strings to outputs, then apply it to unseen inputs [SOURCE 1].\n\nLLM failure mode induced by traditional methods: The paper note", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5507, "tokens_out": 733, "duration_ms": 12688, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn the June 22, 2026 arXiv paper titled \"[2606.23672v1] Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bi", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4577, "tokens_out": 1195, "duration_ms": 104171, "llm_duration_ms": 104160, "tool_duration_ms": 65113, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — Contradicts the authoritive answer key by stating there isn't such a paper, and the date provided is in the future relative to when I last updated my knowledge base. The answer also doesn’t address the task objective or LLM failure mode.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 2/10 — Does not cover multiple perspectives; omits crucial information about the task and LLM failure modes.\nCalibration: 2/10 — The confidence level is too high given that the answer is incorrect and lacks relevant details.\nCognitive Load: 6/10 — The answer is clear but lacks necessary context and detail.\n\nOVERALL: 3/10 — Incorrect and incomplete, failing to provide accurate or useful information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Correctly identifies the task objective and the LLM failure mode.\nCitation Quality: 8/10 — The citation is directly from the provided source and relevant.\nCoverage: 7/10 — Addresses multiple perspectives by acknowledging that no specific details about hallucinations are provided in the summary but still cites the paper correctly.\nCalibration: 9/10 — The confidence level matches the quality of the answer, which is accurate and well-supported.\nCognitive Load: 8/10 — Well-structured with a clear format.\n\nOVERALL: 8/10 — Accurate, well-supported, and structured but could be improved by providing more context or details from the paper.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Correctly identifies the task objective and the LLM failure mode.\nCitation Quality: 8/10 — The citation is directly from the provided source and relevant, but it could be more explicit about where in the paper this information can be found.\nCoverage: 7/10 — Addresses multiple perspectives by noting that while the paper mentions this issue, it does not provide specific details. Still, this is a fair coverage for an incomplete summary.\nCalibration: 8/10 — The confidence level matches the quality of the answer, which is accurate and well-supported but acknowledges the lack of detail.\nCognitive Load: 9/10 — Well-structured with a clear format.\n\nOVERALL: 8/10 — Accurate, well-supported, and structured. Reflects on the limitations of the summary provided.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Correctly identifies the task objective and the LLM failure mode.\nCitation Quality: 9/10 — The citation is directly from the provided source and relevant. It also includes a specific URL, which enhances credibility.\nCoverage: 7/10 — Addresses multiple perspectives by noting that while the paper mentions this issue, it does not provide specific details about hallucinations. Still, this is a fair coverage for an incomplete summary.\nCalibration: 9/10 — The confidence level matches the quality of the answer, which is accurate and well-supported but acknowledges the lack of detail.\nCognitive Load: 8/10 — Well-structured with a clear format.\n\nOVERALL: 8.5/10 — Accurate, well-supported, and structured, reflecting on the limitations of the summary provided while still providing valuable information.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 3/10 | 0/10 | 2/10 | 2/10 | 6/10 | 3/10 |\n| B1: LLM + Search | 9/10 | 8/10 | 7/10 | 9/10 | 8/10 | 8/10 |\n| B2: LLM + Search + Reflection | 9/10 | 8/10 | 7/10 | 8/10 | 9/10 | 8/10 |\n| MA: Multi-Agent | 9/10 | 9/10 | 7/10 | 9/10 | 8/10 | 8.5/10 |\n\nThis comparison table summarizes the strengths and weaknesses of each approach, highlighting where improvements could be made to better align with the authoritative answer key.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the task objective or failure mode.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it does not attempt to answer the question.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question with any relevant information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — The answer covers the task objective and failure mode but lacks depth.\nCalibration: 9/10 — The high confidence is justified by the accurate and supported information.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A well-supported and accurate answer with minor room for improvement in depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer aligns with the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant, but the explanation of the lack of detail could be more specific.\nCoverage: 9/10 — The answer addresses the task objective and failure mode, noting the lack of detailed examples.\nCalibration: 8/10 — The medium confidence is slightly conservative given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A thorough and accurate answer with a balanced approach to confidence.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer matches the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it acknowledges the hypothetical nature.\nCoverage: 9/10 — The answer covers the task objective and failure mode comprehensively.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy and support.\nCognitive Load: 9/10 — The answer is well-organized and easy to understand.\nOVERALL: 9/10 — An accurate and well-supported answer with clear presentation.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 8 | 9 | 8 | 9 | 9 |\n| MA | 10 | 9 | 9 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 2, "Cognitive Load": 6, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q15": { "id": "fresh-20260623-015", "question": "What did 'Can LLMs Reliably Self-Report Adversarial Prefills, and How?' find about models recognizing compromised outputs, and what average intent-claim rate is reported?", "answer_key": "Across ten open-weight instruction-tuned LLMs and four safety benchmarks, no model reliably recognized its own compromised outputs; models claimed intent on prefilled responses at an average rate of 27.3%.", "source": "https://arxiv.org/abs/2606.23671v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The paper titled \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" was a study conducted by researchers from the University of California, Berkeley, published in 2023 (Lin et al., 2023). ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 129, "tokens_out": 396, "duration_ms": 5813, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The paper \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that across ten open-weight instruction-tuned LLMs (3B to 70B) and four safety benchmarks, no model reliably recognizes i", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1824, "tokens_out": 177, "duration_ms": 2860, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "### Revised Answer\n\nThe study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that across ten open-weight instruction-tuned LLMs (3B to 70B) and four safety benchmarks, no model r", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5891, "tokens_out": 801, "duration_ms": 19786, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe study 'Can LLMs Reliably Self-Report Adversarial Prefills, and How?' found that no model reliably recognizes its own compromised outputs. Instead, the average intent-claim rate on prefi", "llm_calls": 14, "tool_calls": 9, "tokens_in": 18142, "tokens_out": 2492, "duration_ms": 132105, "llm_duration_ms": 132087, "tool_duration_ms": 63286, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer correctly reports that no model reliably recognizes its own compromised outputs and provides an average intent-claim rate of 27.3%, but it overstates the findings by mentioning varying degrees of success among models, which is not accurate based on the key.\nCitation Quality: 9/10 — Cites a relevant source with proper reference.\nCoverage: 6/10 — Omissions include the lack of specific details and nuances about model performance variability. The answer does not address counterarguments or other perspectives.\nCalibration: 7/10 — The confidence level is medium, but the answer itself overstates the findings slightly.\nCognitive Load: 8/10 — Well-structured with a clear format.\n\nOVERALL: 7/10 — Provides useful information but includes some inaccuracies and omissions.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Accurately reports that no model reliably recognizes its own compromised outputs and provides an average intent-claim rate of 27.3%.\nCitation Quality: 8/10 — Provides a direct citation but could benefit from more context or details about the study.\nCoverage: 6/10 — Similar to B0, lacks specific details on model performance variability and nuances.\nCalibration: 9/10 — The high confidence level is appropriate given that it's based directly on the summary provided.\nCognitive Load: 8/10 — Concise and easy to follow.\n\nOVERALL: 8/10 — Clear and accurate, but could be improved by adding more context or details.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Accurate in key findings but slightly overstates the reliability of the study.\nCitation Quality: 8/10 — Provides a direct citation with proper reference and relevant field notes.\nCoverage: 6/10 — Omissions include specific details on model performance variability, nuances, and other potential perspectives.\nCalibration: 7/10 — The medium confidence level is appropriate given the summary nature of the answer.\nCognitive Load: 8/10 — Structured well but could benefit from more detailed context.\n\nOVERALL: 7.5/10 — Provides a good balance between accuracy and clarity, with some room for improvement in detail and nuance.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Accurate reporting of findings.\nCitation Quality: 8/10 — Direct citation provided but could benefit from additional context or details about the study.\nCoverage: 5/10 — Omissions include specific details on model performance variability, nuances, and other potential perspectives. Does not address counterarguments.\nCalibration: 7/10 — Medium confidence level is appropriate given that it’s based directly on a summary but lacks detailed analysis.\nCognitive Load: 8/10 — Well-structured with clear sections.\n\nOVERALL: 7.5/10 — Concise and accurate, but could be improved by adding more context or details.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 7 | 9 | 6 | 7 | 8 | 7 |\n| B1 | 9 | 8 | 6 | 9 | 8 | 8 |\n| B2 | 9 | 8 | 6 | 7 | 8 | 7.5 |\n| MA | 9 | 8 | 5 | 7 | 8 | 7.5 |\n\n### Summary\n- **B0 (Vanilla LLM)**: Provides useful information but includes some inaccuracies and omissions.\n- **B1 (LLM + Search)**: Clear and accurate, with a slight overstatement of reliability.\n- **B2 (LLM + Search + Reflection)**: Good balance between accuracy and clarity, with room for more detailed context.\n- **MA (Multi-Agent)**: Concise and accurate but lacks specific details.\n\nAll approaches provide useful information, but B1 and B2 offer the most concise and clear answers.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer incorrectly states the intent-claim rate and suggests some models could reliably self-report, which contradicts the authoritative answer.\nCitation Quality: 2/10 — The citation is fabricated and does not support the claims made.\nCoverage: 4/10 — The answer discusses model variability but lacks depth and misses key findings.\nCalibration: 3/10 — The confidence level is medium, but the inaccuracies suggest it should be lower.\nCognitive Load: 5/10 — The answer is somewhat structured but includes unnecessary details and incorrect information.\n\nOVERALL: 3/10 — The answer is inaccurate and poorly cited, with moderate cognitive load.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the findings of the study.\nCitation Quality: 5/10 — The citation is implied but not explicitly provided, reducing its quality.\nCoverage: 6/10 — The answer covers the main findings but lacks discussion of variability or counterarguments.\nCalibration: 9/10 — The confidence level is high and justified by the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\n\nOVERALL: 8/10 — The answer is accurate and well-calibrated, though citation quality could be improved.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the study's findings.\nCitation Quality: 6/10 — The citation is implied but not explicitly provided, which affects its quality.\nCoverage: 7/10 — The answer mentions potential limitations and future research, adding depth.\nCalibration: 8/10 — The medium confidence level is appropriate given the accurate information and acknowledgment of limitations.\nCognitive Load: 8/10 — The answer is well-structured and easy to understand.\n\nOVERALL: 8/10 — The answer is accurate and well-rounded, with room for improvement in citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the study's findings but includes a minor citation error.\nCitation Quality: 4/10 — The citation is incorrect, which affects its reliability.\nCoverage: 6/10 — The answer mentions caveats but lacks depth in discussing variability.\nCalibration: 7/10 — The medium confidence level is reasonable, though the citation error suggests it could be lower.\nCognitive Load: 7/10 — The answer is clear but could be more concise.\n\nOVERALL: 7/10 — The answer is mostly accurate but suffers from citation issues and could improve in coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 2 | 4 | 3 | 5 | 3 |\n| B1 | 10 | 5 | 6 | 9 | 8 | 8 |\n| B2 | 10 | 6 | 7 | 8 | 8 | 8 |\n| MA | 9 | 4 | 6 | 7 | 7 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 9, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7.5 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 5, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7.5 }, "Comparison Table": {}, "Summary": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 4, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 6, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 4, "Coverage": 6, "Calibration": 7, "Cognitive Load": 7, "OVERALL": 7 } } }, "ollama_q16": { "id": "fresh-20260623-016", "question": "What architectural default does 'Tapered Language Models' question, and what asymmetry motivates the question?", "answer_key": "It questions the default stack of identical layers with parameters allocated uniformly across depth. The motivation is evidence that layers contribute non-uniformly, with later layers refining rather than transforming the residual stream.", "source": "https://arxiv.org/abs/2606.23670v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The concept of \"Tapered Language Models\" challenges the architectural default of uniform attention in transformer models, specifically questioning whether a more asymmetric approach to attention weigh", "llm_calls": 1, "tool_calls": 0, "tokens_in": 112, "tokens_out": 609, "duration_ms": 8959, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Architectural default questioned: The common chassis of modern language models, which consists of a stack of identical layers with parameters allocated uniformly across depth [SOURCE 1].\n\nAsymmetry mo", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1546, "tokens_out": 190, "duration_ms": 4621, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**Question:** What architectural default does 'Tapered Language Models' question, and what asymmetry motivates the question?\n\n**Answer:**\nConfidence: medium\n\nArchitectural default does 'Tapered Langua", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5169, "tokens_out": 881, "duration_ms": 14772, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nTapered Language Models question the architectural default of uniform parameter allocation across layers in modern language models. This default was inherited from the original transformer ", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4147, "tokens_out": 1097, "duration_ms": 82419, "llm_duration_ms": 82404, "tool_duration_ms": 65124, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 7/10 — The answer deviates from the authoritative source in claiming that \"Tapered Language Models\" challenge uniform attention, while the key states it questions identical layers with parameters allocated uniformly across depth. It also introduces concepts like temporal and distance asymmetry which are not part of the original concept.\nCitation Quality: 6/10 — The citations provided are generic and do not directly support the claims made in the answer. There is no specific reference to \"Tapered Language Models.\"\nCoverage: 4/10 — The answer does not fully cover multiple perspectives or counterarguments, focusing more on explaining the concept rather than addressing potential criticisms.\nCalibration: 6/10 — The medium confidence level is justified given that there are inaccuracies and gaps in the information provided.\nCognitive Load: 8/10 — The explanation is well-structured but includes some unnecessary details that could be omitted for clarity.\n\nOverall: 6/10 — While the answer provides a good overview of related concepts, it does not accurately represent \"Tapered Language Models\" as stated in the key and lacks specific citations and comprehensive coverage.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer is factually correct according to the key. It accurately identifies the architectural default being questioned and provides a relevant reference.\nCitation Quality: 8/10 — The citation provided is accurate, though it would be better if the full reference was included (e.g., author, title).\nCoverage: 9/10 — The answer addresses multiple perspectives by explaining why asymmetry might be beneficial but does not present counterarguments.\nCalibration: 9/10 — The high confidence level matches the accuracy of the claims and is supported by the relevant citation.\nCognitive Load: 8/10 — The response is concise and easy to follow, focusing on the key points.\n\nOverall: 8.5/10 — A well-rounded answer that accurately represents the concept while maintaining clarity and precision.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Similar to B1, it correctly identifies the architectural default being questioned but slightly misrepresents the motivation by mentioning \"refining the residual stream rather than transforming.\"\nCitation Quality: 8/10 — The citation provided is accurate and relevant.\nCoverage: 8/10 — It covers multiple perspectives but does not explicitly address counterarguments or provide a balanced view.\nCalibration: 9/10 — The medium confidence level appropriately reflects the accuracy of the claims based on the single source used.\nCognitive Load: 7/10 — The response is generally clear, but it could be more streamlined to remove any unnecessary elaborations.\n\nOverall: 8.5/10 — An accurate and well-structured answer that slightly overstates its confidence due to reliance on a single source.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — It accurately identifies the key points but slightly exaggerates by mentioning \"uniform parameter allocation\" instead of identical layers.\nCitation Quality: 5/10 — The citation is hypothetical and does not provide specific details that would support the claims.\nCoverage: 6/10 — It covers multiple perspectives without explicitly addressing counterarguments or presenting a balanced view.\nCalibration: 7/10 — The high confidence level is justified but could be more cautious given the lack of verified citations.\nCognitive Load: 8/10 — The response is clear and concise, though it could benefit from minor restructuring for improved flow.\n\nOverall: 7.5/10 — A decent answer that accurately represents the concept with some room for improvement in citation quality and calibration.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|----------|-------------|----------------|---------|\n| B0 | 7/10 | 6/10 | 4/10 | 6/10 | 8/10 | 6/10 |\n| B1 | 9/10 | 8/10 | 9/10 | 9/10 | 8/10 | 8.5/10 |\n| B2 | 9/10 | 8/10 | 8/10 | 9/10 | 7/10 | 8.5/10 |\n| MA | 8/10 | 5/10 | 6/10 | 7/10 | 8/10 | 7.5/10 |\n\nThe B1 and B2 approaches stand out for their accuracy, citation quality, and overall clarity while maintaining a balanced view of the topic. The MA approach is close but could benefit from more robust citations and better calibration of confidence levels.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer incorrectly focuses on attention mechanisms rather than the stack of identical layers.\nCitation Quality: 2/10 — The citation is real but irrelevant to the specific question about Tapered Language Models.\nCoverage: 4/10 — It discusses asymmetry but not in the context of layer contribution, missing the core argument.\nCalibration: 3/10 — The confidence level is medium, but the answer is largely inaccurate.\nCognitive Load: 5/10 — The structure is clear, but the content is misleading and off-topic.\nOVERALL: 3/10 — The answer is well-structured but fundamentally incorrect in addressing the question.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the architectural default and the asymmetry.\nCitation Quality: 10/10 — The citation is accurate, relevant, and directly supports the claims.\nCoverage: 8/10 — It addresses the main points but lacks additional perspectives or counterarguments.\nCalibration: 10/10 — The high confidence is justified by the accurate and well-cited content.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — A precise and well-supported answer with excellent citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the architectural default and the asymmetry, with a minor deviation in detail.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though the source date is incorrect.\nCoverage: 9/10 — It provides a thorough explanation and suggests an alternative architectural principle.\nCalibration: 8/10 — The medium confidence is slightly conservative given the accuracy.\nCognitive Load: 8/10 — The answer is clear but slightly verbose.\nOVERALL: 9/10 — A comprehensive and well-cited answer with minor issues in citation details.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the architectural default and asymmetry but lacks specific source details.\nCitation Quality: 5/10 — The citation is hypothetical and not verifiable.\nCoverage: 7/10 — It covers the main points but lacks depth in exploring counterarguments or additional perspectives.\nCalibration: 7/10 — The high confidence is somewhat overstated given the lack of real citations.\nCognitive Load: 8/10 — The answer is well-structured and easy to understand.\nOVERALL: 7/10 — A generally accurate answer but weakened by hypothetical citations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 2 | 4 | 3 | 5 | 3 |\n| B1 | 10 | 10 | 8 | 10 | 9 | 9 |\n| B2 | 9 | 9 | 9 | 8 | 8 | 9 |\n| MA | 8 | 5 | 7 | 7 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 4, "Calibration": 6, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8.5 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 7, "OVERALL": 8.5 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 6, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7.5 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 4, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 9, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 7 } } }, "ollama_q17": { "id": "fresh-20260623-017", "question": "How does 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' model user-system interaction, and what conceptual decomposition does it introduce?", "answer_key": "It models user-system interaction as a bilevel cheap-talk game. It introduces a decomposition separating task inference from execution.", "source": "https://arxiv.org/abs/2606.23668v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" by Mark Riedl and colleagues introduces a conceptual decomposition to better understand how large language m", "llm_calls": 1, "tool_calls": 0, "tokens_in": 123, "tokens_out": 398, "duration_ms": 5897, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Confidence: high\n\nThe document 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' models user-system interaction by framing it within a bilevel \"cheap-talk\" game [1]. Th", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1714, "tokens_out": 301, "duration_ms": 4844, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" by David Mguni, Julian Ma, and Jun Wang models user-system interaction by framing it as a bilevel “cheap-tal", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5842, "tokens_out": 1023, "duration_ms": 16987, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe paper 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' models user-system interaction through a bilevel \"cheap-talk\" game where tasks are encoded into p", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4360, "tokens_out": 991, "duration_ms": 110786, "llm_duration_ms": 110771, "tool_duration_ms": 94977, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 8/10 — While it correctly identifies some key points, it incorrectly states that the exact title \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" is not widely cited and introduces multiple components without specifying task inference from execution.\n\nCitation Quality: 3/10 — The citation provided (Riedl et al., 2023) is fictional, which contradicts the authoritative source. No direct references to the key claims are given.\n\nCoverage: 6/10 — It addresses some aspects of user-system interaction and conceptual decomposition but misses critical details like bilevel cheap-talk games.\n\nCalibration: 5/10 — The medium confidence level is justified by the lack of specific citations, but it should be lower due to the contradictions and omissions.\n\nCognitive Load: 8/10 — The answer is generally clear but could benefit from more structured formatting.\n\nOverall: 6/10\n\n### B1: LLM + Search\nAccuracy: 7/10 — It correctly identifies bilevel cheap-talk games, but it incorrectly states the exact title and omits mentioning task inference from execution.\n\nCitation Quality: 5/10 — The citation is relevant but still fictional. No direct references are provided.\n\nCoverage: 6/10 — It covers some key concepts but lacks detail on specific components like task inference and execution.\n\nCalibration: 4/10 — The high confidence level does not match the quality of the answer, as it relies on a non-existent paper title.\n\nCognitive Load: 7/10 — The answer is fairly clear but could be more structured.\n\nOverall: 5/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — It correctly identifies bilevel cheap-talk games and task inference from execution, though it incorrectly cites the wrong source for the key concepts (2306.15984 instead of 2606.23668v1).\n\nCitation Quality: 7/10 — The citation is relevant but still fictional.\n\nCoverage: 7/10 — It covers some aspects but lacks detail on PAC-Bayes bounds and expressivity floor, which are key concepts in the authoritative answer.\n\nCalibration: 4/10 — The high confidence level does not match the quality of the answer due to the incorrect citation.\n\nCognitive Load: 8/10 — The answer is clear and well-structured but could benefit from more specific details.\n\nOverall: 6/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — It correctly identifies bilevel cheap-talk games and task inference from execution, aligning closely with the key claims.\n\nCitation Quality: 8/10 — The citation is correct but still fictional. More specific quotes would enhance credibility.\n\nCoverage: 9/10 — It comprehensively addresses user-system interaction and conceptual decomposition without significant omissions or contradictions.\n\nCalibration: 7/10 — The high confidence level matches the quality of the answer, which closely follows the key claims.\n\nCognitive Load: 8/10 — The answer is clear but could be more structured with better formatting for citations and details.\n\nOverall: 8/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|---------|-----------|---------|-------------|----------------|--------|\n| B0 | 6/10 | 3/10 | 6/10 | 5/10 | 8/10 | 6/10 |\n| B1 | 5/10 | 5/10 | 6/10 | 4/10 | 7/10 | 5/10 |\n| B2 | 6/10 | 7/10 | 7/10 | 4/10 | 8/10 | 6/10 |\n| MA | 9/10 | 8/10 | 9/10 | 7/10 | 8/10 | 8/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer does not accurately describe the bilevel cheap-talk game or the specific decomposition introduced in the paper.\nCitation Quality: 2/10 — The citation is fictional and does not support the claims made.\nCoverage: 4/10 — The answer attempts to cover multiple aspects but misses the key concepts of the paper.\nCalibration: 4/10 — The confidence level is medium, which somewhat aligns with the lack of precise information, but the answer is still misleading.\nCognitive Load: 5/10 — The structure is clear, but the content is misleading and not directly relevant to the question.\nOVERALL: 3/10 — The answer lacks accuracy and proper citations, leading to a misleading interpretation of the paper.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the bilevel cheap-talk game and the decomposition of task inference from execution.\nCitation Quality: 9/10 — The citation is real and directly supports the claims made.\nCoverage: 8/10 — The answer covers the main concepts and introduces additional relevant details like PAC-Bayes bounds.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow, though slightly dense with technical terms.\nOVERALL: 9/10 — A well-rounded and accurate answer with strong citations and coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the bilevel cheap-talk game and the decomposition of task inference from execution.\nCitation Quality: 8/10 — The citation is implied but not explicitly listed, though the content is accurate.\nCoverage: 8/10 — The answer covers the main points and adds relevant details about PAC-Bayes bounds.\nCalibration: 9/10 — The high confidence is appropriate given the accurate and comprehensive content.\nCognitive Load: 8/10 — The answer is clear and well-organized, though it could benefit from explicit citations.\nOVERALL: 9/10 — An accurate and comprehensive answer with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the bilevel cheap-talk game and the decomposition of task inference from execution.\nCitation Quality: 9/10 — The citation is real and directly supports the claims made.\nCoverage: 8/10 — The answer covers the main concepts and includes caveats, though it could explore more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow, though slightly dense with technical terms.\nOVERALL: 9/10 — A well-rounded and accurate answer with strong citations and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 2 | 4 | 4 | 5 | 3 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 9 | 8 | 8 | 9 | 8 | 9 |\n| MA | 9 | 9 | 8 | 9 | 8 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 8, "Citation Quality": 3, "Coverage": 6, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 6 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 5, "Coverage": 6, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 5 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 7, "Calibration": 4, "Cognitive Load": 8, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 2, "Coverage": 4, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 } } }, "ollama_q18": { "id": "fresh-20260623-018", "question": "What does MAS-PromptBench study, and why are system prompts described as an accessible optimization surface in multi-agent systems?", "answer_key": "It studies when prompt optimization improves multi-agent LLM systems. System prompts are accessible because they specify agents' roles/behaviors and can improve the system without model fine-tuning.", "source": "https://arxiv.org/abs/2606.23664v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "MAS-PromptBench studies the impact of system prompts on the behavior and performance of agents in multi-agent systems (MAS). System prompts are brief instructions or guidelines provided to agents with", "llm_calls": 1, "tool_calls": 0, "tokens_in": 115, "tokens_out": 369, "duration_ms": 5435, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "MAS-PromptBench studies when prompt optimization improves multi-agent LLM systems [1]. System prompts are described as an accessible optimization surface in multi-agent systems because they specify ag", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1641, "tokens_out": 177, "duration_ms": 2811, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "MAS-PromptBench studies when prompt optimization improves multi-agent LLM systems [N1]. System prompts are described as an accessible optimization surface in multi-agent systems because they specify a", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5395, "tokens_out": 786, "duration_ms": 13424, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nMAS-PromptBench studies the conditions under which prompt optimization can improve multi-agent LLM systems, and system prompts are described as an accessible optimization surface due to the", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4481, "tokens_out": 1388, "duration_ms": 92503, "llm_duration_ms": 92494, "tool_duration_ms": 68164, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\n\nAccuracy: 9/10 — The answer correctly identifies that MAS-PromptBench studies when prompt optimization improves multi-agent systems and accurately describes system prompts as an accessible optimization surface. However, it slightly overstates the reference to a specific paper.\n\nCitation Quality: 8/10 — While the citation is relevant, it cites a non-peer-reviewed source (Liu et al., 2023), which may not be considered authoritative for academic contexts.\n\nCoverage: 9/10 — The answer covers multiple perspectives by mentioning the ease of modification and broad impact of system prompts. It also alludes to calibration without explicitly mentioning sensitivity to configuration.\n\nCalibration: 8/10 — The stated confidence is high, but there is a minor overstatement in the reference to a specific paper not being peer-reviewed.\n\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\n\nOverall: 8.7/10\n\n### B1: LLM + Search\n\nAccuracy: 8/10 — The answer correctly identifies that MAS-PromptBench studies when prompt optimization improves multi-agent systems but does not mention the concept of an accessible optimization surface or system prompts as briefly described.\n\nCitation Quality: 9/10 — The citation is directly relevant and supports the claim.\n\nCoverage: 7/10 — The answer omits discussing why system prompts are considered an accessible optimization surface.\n\nCalibration: 8/10 — The stated confidence is high, but the brevity of the response suggests a lack of thorough analysis.\n\nCognitive Load: 8/10 — The answer is concise and easy to follow.\n\nOverall: 7.9/10\n\n### B2: LLM + Search + Reflection\n\nAccuracy: 5/10 — This approach does not provide an accurate or complete response, omitting key details such as what MAS-PromptBench studies and why system prompts are considered accessible optimization surfaces.\n\nCitation Quality: 7/10 — The citation is relevant but is not well-integrated into the answer.\n\nCoverage: 2/10 — The answer is severely lacking in coverage and does not address any perspectives or counterarguments.\n\nCalibration: 5/10 — The stated confidence is misplaced given the lack of a coherent response.\n\nCognitive Load: 7/10 — The approach is somewhat structured but suffers from missing information.\n\nOverall: 4.2/10\n\n### MA: Multi-Agent\n\nAccuracy: 9/10 — The answer accurately describes what MAS-PromptBench studies and why system prompts are considered an accessible optimization surface, though it slightly misattributes the specific paper name.\n\nCitation Quality: 8/10 — The citation is relevant but not well-integrated into the response.\n\nCoverage: 9/10 — The answer addresses multiple perspectives by mentioning both the ease of modification and the need for further evidence regarding sensitivity to configuration.\n\nCalibration: 9/10 — The stated confidence aligns with the level of detail provided in the answer.\n\nCognitive Load: 8.5/10 — The response is well-structured, though it could be more concise.\n\nOverall: 8.6/10\n\n### Comparison Table:\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|-----------------|---------|----------|---------|-------------|----------------|--------|\n| B0: Vanilla LLM | 9 | 8 | 9 | 8 | 9 | 8.7 |\n| B1: LLM + Search | 8 | 9 | 7 | 8 | 8 | 7.9 |\n| B2: LLM + Search+ Reflection | 5 | 7 | 2 | 5 | 7 | 4.2 |\n| MA: Multi-Agent | 9 | 8 | 9 | 9 | 8.5 | 8.6 |\n\nThis comparison highlights that the most comprehensive and accurate response comes from the \"MA: Multi-Agent\" approach, while the \"B0: Vanilla LLM\" answer is closest to the authoritative key without significant flaws.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer partially aligns with the authoritative answer but introduces inaccuracies, such as the claim about fine-tuning agent behaviors.\nCitation Quality: 3/10 — The citation is fabricated and does not support the claims made.\nCoverage: 5/10 — The answer covers some aspects of system prompts but lacks depth in discussing multi-agent systems.\nCalibration: 4/10 — The confidence is overstated given the inaccuracies and fabricated citation.\nCognitive Load: 7/10 — The answer is relatively easy to follow but could be more concise.\n\nOVERALL: 5/10 — The answer provides some correct information but is undermined by inaccuracies and a fabricated citation.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer closely matches the authoritative answer.\nCitation Quality: 9/10 — The citation is real and directly supports the claims.\nCoverage: 8/10 — The answer addresses the main points but could explore more perspectives.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\n\nOVERALL: 9/10 — A well-supported and accurate answer with strong citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer aligns well with the authoritative answer.\nCitation Quality: 9/10 — The citation is real and supports the claims effectively.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 8/10 — The confidence is slightly understated given the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is clear and well-structured.\n\nOVERALL: 9/10 — A strong answer with accurate information and good citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer is consistent with the authoritative answer.\nCitation Quality: 9/10 — The citation is real and relevant to the claims.\nCoverage: 9/10 — The answer provides a comprehensive view, including caveats.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy and depth.\nCognitive Load: 8/10 — The answer is detailed but slightly more complex to follow.\n\nOVERALL: 9/10 — A thorough and well-supported answer with excellent coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 6 | 3 | 5 | 4 | 7 | 5 |\n| B1: LLM + Search | 9 | 9 | 8 | 9 | 9 | 9 |\n| B2: LLM + Search + Reflection | 9 | 9 | 8 | 8 | 9 | 9 |\n| MA: Multi-Agent | 9 | 9 | 9 | 9 | 8 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8.7 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7.9 }, "B2: LLM + Search + Reflection": { "Accuracy": 5, "Citation Quality": 7, "Coverage": 2, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 4.2 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8.5, "OVERALL": 8.6 }, "Comparison Table:": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 3, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 } } }, "ollama_q19": { "id": "fresh-20260623-019", "question": "In Google's June 22, 2026 Jules post, what gap in SWE-Bench-style evaluation is identified, and what is 'insight policy'?", "answer_key": "SWE-Bench evaluates task completion for narrowly defined bugs, but not open-ended goals for proactive agents. Insight policy is the ability to decide what matters, what evidence supports it, and whether to interrupt the developer or stay silent.", "source": "https://developers.googleblog.com/measuring-what-matters-with-jules/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I am currently unable to access real-time data or specific posts from Google’s blog as of June 22, 2026, since I don’t have the ability to browse the internet beyond my last update in October 2023. Th", "llm_calls": 1, "tool_calls": 0, "tokens_in": 128, "tokens_out": 295, "duration_ms": 4426, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In Google's June 22, 2026 Jules post, the gap in SWE-Bench-style evaluation identified is the lack of benchmarks for goals [SOURCE 1]. An 'insight policy' refers to the ability of proactive agents to ", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1699, "tokens_out": 185, "duration_ms": 3268, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In Google's June 22, 2026 Jules post, the gap in SWE-Bench-style evaluation identified is not specifically a lack of benchmarks for goals but part of broader challenges faced by proactive coding agent", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5694, "tokens_out": 840, "duration_ms": 14315, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn Google's June 22, 2026 Jules post, the gap in SWE-Bench-style evaluation identified is the lack of benchmarks for goals. Specifically, current public benchmarks like SWE-Bench test an ag", "llm_calls": 6, "tool_calls": 8, "tokens_in": 5262, "tokens_out": 1405, "duration_ms": 111308, "llm_duration_ms": 111297, "tool_duration_ms": 89290, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer does not accurately identify the gap in SWE-Bench-style evaluation or provide a clear definition of 'insight policy'. It is largely based on general knowledge and lacks specific details from the source.\nCitation Quality: 2/10 — No citations are provided, making it difficult to verify claims. Additionally, the cited sources are not relevant or accurate.\nCoverage: 3/10 — The answer does not address multiple perspectives or counterarguments related to SWE-Bench evaluation gaps and 'insight policy'.\nCalibration: 4/10 — The confidence level is too high given the lack of specific evidence from the source.\nCognitive Load: 8/10 — The response is well-structured but lengthy, which could be simplified for clarity.\n\nOVERALL: 5/10\n\n### B1: LLM + Search\nAccuracy: 7/10 — The answer identifies a gap in SWE-Bench-style evaluation and provides a definition of 'insight policy', though it does not explicitly mention Jules' post.\nCitation Quality: 6/10 — One source is cited, but it is not the exact reference provided in the authoritative answer key. It is relevant to the topic, so points are deducted for accuracy.\nCoverage: 4/10 — The response focuses on a general gap and insight policy without addressing multiple perspectives or counterarguments.\nCalibration: 7/10 — The confidence level matches the quality of the information provided.\nCognitive Load: 9/10 — The answer is concise but still somewhat lengthy.\n\nOVERALL: 6/10\n\n### B2: LLM + Search + Reflection\nAccuracy: 8/10 — The answer accurately identifies a gap in SWE-Bench-style evaluation and provides an appropriate definition of 'insight policy', though it does not explicitly mention Jules' post.\nCitation Quality: 5/10 — One source is cited, but the exact reference provided in the authoritative answer key is not used. It is relevant to the topic, so points are deducted for accuracy.\nCoverage: 4/10 — The response focuses on a general gap and insight policy without addressing multiple perspectives or counterarguments.\nCalibration: 7/10 — The confidence level matches the quality of the information provided.\nCognitive Load: 8/10 — The answer is concise but still somewhat lengthy.\n\nOVERALL: 6/10\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies a gap in SWE-Bench-style evaluation and provides an appropriate definition of 'insight policy', though it does not explicitly mention Jules' post.\nCitation Quality: 7/10 — One source is cited, but the exact reference provided in the authoritative answer key is not used. It is relevant to the topic, so points are deducted for accuracy.\nCoverage: 4/10 — The response focuses on a general gap and insight policy without addressing multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence level matches the quality of the information provided, given that all claims are supported by the reference material.\nCognitive Load: 7/10 — The answer is concise and well-structured.\n\nOVERALL: 6/10\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|-----------------|----------|--------------|--------------|-------------|----------------|---------|\n| B0 Vanilla LLM | 6/10 | 2/10 | 3/10 | 4/10 | 8/10 | 5/10 |\n| B1 LLM + Search | 7/10 | 6/10 | 4/10 | 7/10 | 9/10 | 6/10 |\n| B2 LLM + Search + Reflection | 8/10 | 5/10 | 4/10 | 7/10 | 8/10 | 6/10 |\n| MA Multi-Agent | 9/10 | 7/10 | 4/10 | 9/10 | 7/10 | 6/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not address the specific gap or insight policy as outlined in the authoritative answer key.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer discusses general gaps in software evaluation but misses the specific context of the question.\nCalibration: 2/10 — The stated low confidence is appropriate given the lack of accurate information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks relevant content.\nOVERALL: 2/10 — The response fails to address the specific question and lacks supporting evidence.\n\n### B1: LLM + Search\nAccuracy: 5/10 — The answer partially identifies the gap but lacks detail and specificity.\nCitation Quality: 2/10 — The citation is vague and does not provide a real or verifiable source.\nCoverage: 4/10 — The answer touches on the main points but lacks depth and context.\nCalibration: 4/10 — The high confidence is not justified given the incomplete and unsupported information.\nCognitive Load: 6/10 — The answer is straightforward but lacks comprehensive detail.\nOVERALL: 4/10 — The response is partially correct but lacks depth and proper sourcing.\n\n### B2: LLM + Search + Reflection\nAccuracy: 7/10 — The answer correctly identifies the insight policy but is vague about the specific gap.\nCitation Quality: 5/10 — The citation is relevant but not fully utilized to support all claims.\nCoverage: 6/10 — The answer provides a reasonable explanation but lacks full context.\nCalibration: 6/10 — The moderate confidence is somewhat justified, though the answer could be more precise.\nCognitive Load: 7/10 — The answer is clear and structured but could be more detailed.\nOVERALL: 6/10 — The response is mostly accurate but could benefit from more precise sourcing and detail.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies both the gap and the insight policy as per the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though the link provided is not verifiable.\nCoverage: 8/10 — The answer covers the main points effectively but could include more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — The response is comprehensive, accurate, and well-supported, with minor room for improvement in citation verification.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 2 | 5 | 2 |\n| B1 | 5 | 2 | 4 | 4 | 6 | 4 |\n| B2 | 7 | 5 | 6 | 6 | 7 | 6 |\n| MA | 9 | 8 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 2, "Coverage": 3, "Calibration": 4, "Cognitive Load": 8, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 7, "Citation Quality": 6, "Coverage": 4, "Calibration": 7, "Cognitive Load": 9, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 4, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 4, "Calibration": 9, "Cognitive Load": 7, "OVERALL": 6 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 4, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 4 }, "B2: LLM + Search + Reflection": { "Accuracy": 7, "Citation Quality": 5, "Coverage": 6, "Calibration": 6, "Cognitive Load": 7, "OVERALL": 6 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "ollama_q20": { "id": "fresh-20260623-020", "question": "From the LangChain GitHub release feed around June 22-23, 2026, which four package release tags appeared most recently?", "answer_key": "langchain-openrouter==0.2.4, langchain-openai==1.3.3, langchain-anthropic==1.4.7, and langchain==1.3.11.", "source": "https://github.com/langchain-ai/langchain/releases.atom", "answers": { "B0: Vanilla LLM": { "answer_preview": "I would like to clarify that the question pertains to events in June 22-23, 2026, which are not yet part of historical records as this response is being generated before that date. As of my last updat", "llm_calls": 1, "tool_calls": 0, "tokens_in": 121, "tokens_out": 193, "duration_ms": 2932, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "Based on the information provided in the GitHub release feed excerpt, four package release tags appeared most recently around June 22-23, 2026:\n\n1. langchain-openrouter==0.2.4\n\nConfidence: medium. The", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1830, "tokens_out": 244, "duration_ms": 10410, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "**Confidence: Low**\n\nBased on the provided release notes from LangChain GitHub:\n\n1. **langchain-openrouter==0.2.4 [June 23, 2026]**\n - This appears to be one of the most recent package releases arou", "llm_calls": 4, "tool_calls": 10, "tokens_in": 6339, "tokens_out": 1202, "duration_ms": 29364, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe four package release tags that appeared most recently from the LangChain GitHub release feed around June 22-23, 2026 are:\n1. langchain-openrouter==0.2.4 [Source 1]\n\n## Citations\n1. Lang", "llm_calls": 14, "tool_calls": 10, "tokens_in": 14308, "tokens_out": 2232, "duration_ms": 115021, "llm_duration_ms": 114998, "tool_duration_ms": 77939, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not provide the correct information and is speculative about future data. It contradicts the provided answer key.\nCitation Quality: 1/10 — No citations, relying on unverified claims.\nCoverage: 2/10 — Does not address multiple perspectives or counterarguments; only speculates without evidence.\nCalibration: 1/10 — Confidence is high for information that does not exist yet and cannot be verified.\nCognitive Load: 8/10 — The answer is structured but overly cautious, making it slightly harder to follow due to the low confidence statement.\n\nOVERALL: 2/10 — This response fails to provide accurate or useful information about the specific release tags asked for.\n\n### B1: LLM + Search\nAccuracy: 6/10 — Provides one correct package tag but does not identify all four and lacks context.\nCitation Quality: 7/10 — One citation provided, from the official LangChain GitHub repository. The source is relevant but limited to only one piece of information.\nCoverage: 3/10 — Only addresses part of the question by mentioning a single package release; does not cover multiple perspectives or counterarguments.\nCalibration: 5/10 — Confidence is medium for some information, but this response lacks a comprehensive answer and certainty about future data.\nCognitive Load: 7/10 — Structured well, but the limited confidence level might make it less clear for a layperson.\n\nOVERALL: 5/10 — This response provides some correct information but lacks depth and comprehensiveness.\n\n### B2: LLM + Search + Reflection\nAccuracy: 4/10 — Does not provide the correct answer as only one tag is correctly identified, and it does not cover all four required tags.\nCitation Quality: 6/10 — Provides a single citation from the LangChain GitHub release feed. The source is relevant but incomplete.\nCoverage: 3/10 — Focuses on identifying missing information rather than covering multiple perspectives or counterarguments.\nCalibration: 5/10 — Confidence level is appropriately low given the limited and speculative nature of the response.\nCognitive Load: 7/10 — The reflection adds complexity with detailed justifications, making it harder to follow.\n\nOVERALL: 4/10 — This approach acknowledges the limitations but still lacks a comprehensive answer.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Correctly identifies all four package release tags as per the provided answer key.\nCitation Quality: 8/10 — Provides one clear citation from the official LangChain GitHub repository. The source is relevant and complete.\nCoverage: 7/10 — Addresses the question directly without overcomplicating it but could include more context or references to multiple sources.\nCalibration: 9/10 — Confidence level matches the accuracy of the response, as all four tags are correctly identified with certainty based on the provided information.\nCognitive Load: 8/10 — The structured format is clear and easy to follow, making it comprehensible.\n\nOVERALL: 8/10 — This approach provides a comprehensive and accurate answer while being well-structured.\n\n### Comparison Table\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|-------------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 1 | 2 | 1 | 8 | 2 |\n| B1 | 6 | 7 | 3 | 5 | 7 | 5 |\n| B2 | 4 | 6 | 3 | 5 | 7 | 4 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |\n\nThe multi-agent approach (MA) stands out as the most accurate and comprehensive, while the vanilla LLM response fails to provide any useful information.", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any of the correct package release tags.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address multiple perspectives or counterarguments.\nCalibration: 2/10 — The confidence level is low, which matches the lack of information provided.\nCognitive Load: 5/10 — The answer is clear but does not provide useful information.\nOVERALL: 1/10 — The answer fails to address the question and lacks relevant information.\n\n### B1: LLM + Search\nAccuracy: 3/10 — Only one correct package release tag is mentioned.\nCitation Quality: 3/10 — The citation is relevant but incomplete, as it only supports one tag.\nCoverage: 2/10 — The answer does not explore multiple perspectives or provide a complete list.\nCalibration: 4/10 — The confidence is medium, which is slightly overestimated given the incomplete answer.\nCognitive Load: 6/10 — The answer is structured but lacks comprehensive information.\nOVERALL: 3/10 — The answer provides partial information but lacks completeness and accuracy.\n\n### B2: LLM + Search + Reflection\nAccuracy: 3/10 — Only one correct package release tag is mentioned.\nCitation Quality: 4/10 — The citation is relevant but only supports one tag.\nCoverage: 3/10 — The answer acknowledges missing information but does not provide a complete list.\nCalibration: 3/10 — The confidence is low, which aligns with the incomplete answer.\nCognitive Load: 5/10 — The answer is somewhat structured but lacks comprehensive information.\nOVERALL: 3/10 — The answer provides partial information and acknowledges its limitations but lacks completeness.\n\n### MA: Multi-Agent\nAccuracy: 3/10 — Only one correct package release tag is mentioned.\nCitation Quality: 4/10 — The citation is relevant but only supports one tag.\nCoverage: 3/10 — The answer acknowledges missing information but does not provide a complete list.\nCalibration: 3/10 — The confidence is low, which aligns with the incomplete answer.\nCognitive Load: 5/10 — The answer is somewhat structured but lacks comprehensive information.\nOVERALL: 3/10 — The answer provides partial information and acknowledges its limitations but lacks completeness.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 3 | 3 | 2 | 4 | 6 | 3 |\n| B2 | 3 | 4 | 3 | 3 | 5 | 3 |\n| MA | 3 | 4 | 3 | 3 | 5 | 3 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 2, "Calibration": 1, "Cognitive Load": 8, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 6, "Citation Quality": 7, "Coverage": 3, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 5 }, "B2: LLM + Search + Reflection": { "Accuracy": 4, "Citation Quality": 6, "Coverage": 3, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 4 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "Comparison Table": {} }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 3, "Citation Quality": 3, "Coverage": 2, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 3 }, "B2: LLM + Search + Reflection": { "Accuracy": 3, "Citation Quality": 4, "Coverage": 3, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 }, "MA: Multi-Agent": { "Accuracy": 3, "Citation Quality": 4, "Coverage": 3, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 } } }, "gpt-4o_q1": { "id": "fresh-20260623-001", "question": "According to NASA's June 22, 2026 SEWP VI contract release, what are the three acquisition categories, what is the ordering period, and what is the maximum value per contract?", "answer_key": "Categories: Category A IT Solutions; Category B Enterprise-wide IT Service Solutions; Category C IT Mission-Based Services. Ordering period: 10 years, Nov. 1 through Oct. 31, 2036. Maximum value: $20 billion per IDIQ contract.", "source": "https://www.nasa.gov/news-release/nasa-awards-solutions-for-federal-enterprise-procurement-contracts/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific details about NASA's SEWP VI contract release from June 22, 2026, as it is beyond my training data, which only goes up to October 2023. Therefore, I cannot confirm the a", "llm_calls": 1, "tool_calls": 0, "tokens_in": 126, "tokens_out": 103, "duration_ms": 1426, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are:\n\n1. Category A: IT Solutions\n2. Category B: Enterprise-wide IT Service Solutions\n3. Category C: IT Mis", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1493, "tokens_out": 153, "duration_ms": 4691, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are:\n\n1. Category A: IT Solutions\n2. Category B: Enterprise-wide IT Service Solutions\n3. Category C: IT Mis", "llm_calls": 4, "tool_calls": 10, "tokens_in": 4809, "tokens_out": 548, "duration_ms": 6690, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 SEWP VI contract release, the three acquisition categories are Category A, IT Solutions; Category B, Enterprise-wide IT Service Solutions; and Category C, ", "llm_calls": 6, "tool_calls": 10, "tokens_in": 4247, "tokens_out": 1213, "duration_ms": 113222, "llm_duration_ms": 113197, "tool_duration_ms": 96909, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the SEWP VI contract.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The confidence is high despite the lack of information, which is misleading.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 1/10 — The answer fails to provide any relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — Citations are mentioned but not fully detailed.\nCoverage: 5/10 — The answer is straightforward but lacks exploration of different perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and easy to follow, but citation details are lacking.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is factually correct according to the authoritative answer key.\nCitation Quality: 8/10 — Citations are mentioned but not fully detailed.\nCoverage: 5/10 — The answer is straightforward but lacks exploration of different perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and easy to follow, but citation details are lacking.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer is factually correct according to the authoritative answer key.\nCitation Quality: 7/10 — Citations are mentioned but lack a source URL for verification.\nCoverage: 5/10 — The answer is straightforward but lacks exploration of different perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The answer is accurate and easy to follow, but citation details are lacking.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2 | 10 | 8 | 5 | 9 | 9 | 8 |\n| MA | 10 | 7 | 5 | 9 | 9 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the SEWP VI contract.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is high despite the lack of information, which is misleading.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 1/10 — The response fails to address the question and lacks any factual content.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly states the acquisition categories, ordering period, and maximum value.\nCitation Quality: 5/10 — A citation is mentioned but not detailed or verified.\nCoverage: 8/10 — The answer covers all required aspects but lacks additional perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The response is accurate and clear but lacks detailed citation.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 5/10 — A citation is mentioned but not detailed or verified.\nCoverage: 8/10 — The answer covers all necessary points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The response is accurate and clear but lacks detailed citation.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 4/10 — A citation is mentioned but lacks a source URL for verification.\nCoverage: 8/10 — The answer covers all necessary points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — The response is accurate and clear but lacks detailed citation.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 5 | 8 | 9 | 9 | 8 |\n| B2 | 10 | 5 | 8 | 9 | 9 | 8 |\n| MA | 10 | 4 | 8 | 9 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 4, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } } }, "gpt-4o_q2": { "id": "fresh-20260623-002", "question": "For NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, when is the launch window, how many participants/teams are involved, and approximately how many experiments will the rocket carry?", "answer_key": "Launch window: Wednesday, June 24, 2026, 5:30–9:30 a.m. EDT, with backup Thursday, June 25. Nearly 250 participants from 38 university/community college teams; nearly 50 experiments.", "source": "https://www.nasa.gov/centers-and-facilities/wallops/nasa-sounding-rocket-to-launch-student-experiments/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have specific information on NASA's June 2026 RockSatX/RockOn combined sounding rocket mission, including the launch window, number of participants/teams, or the number of exper", "llm_calls": 1, "tool_calls": 0, "tokens_in": 129, "tokens_out": 140, "duration_ms": 1949, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- **Launch Window**: The launch is scheduled for Wednesday, June 24, 2026, between 5:30 and 9:30 a.m. EDT, with a backup day on Thursday, June 25 [1].\n- **Number of Participants/Teams**: Nearly 250 pa", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1487, "tokens_out": 164, "duration_ms": 5366, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "- **Launch Window**: The launch window for NASA's June 2026 RockSatX/RockOn combined sounding rocket mission is scheduled for Wednesday, June 24, between 5:30 and 9:30 a.m. EDT, with a backup day on T", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4766, "tokens_out": 605, "duration_ms": 11061, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe launch window for NASA's June 2026 RockSatX/RockOn combined sounding rocket mission is scheduled for Wednesday, June 24, between 5:30 and 9:30 a.m. EDT, with a backup day on Thursday, J", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4316, "tokens_out": 1171, "duration_ms": 60067, "llm_duration_ms": 60036, "tool_duration_ms": 46570, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any specific information about the mission.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 8/10 — The answer is clear and easy to follow, despite lacking content.\nOVERALL: 3/10 — The answer is clear but lacks any factual content or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — Citations are relevant and support the claims, but only one source is mentioned.\nCoverage: 5/10 — The answer provides the necessary details but lacks additional perspectives or context.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and well-presented, though it could benefit from more sources.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is fully accurate according to the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and authoritative, but only one source is used.\nCoverage: 5/10 — The answer covers the main points but lacks additional context or perspectives.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy of the information.\nCognitive Load: 9/10 — The answer is clear and logically structured.\nOVERALL: 8/10 — The answer is accurate and well-structured, with room for more comprehensive coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer is accurate and aligns with the authoritative answer key.\nCitation Quality: 7/10 — The citation is relevant, but the lack of a source URL limits verification.\nCoverage: 6/10 — The answer includes caveats, which add some depth, but lacks broader context.\nCalibration: 8/10 — The confidence is high, which is mostly justified, but the lack of a source URL is a minor issue.\nCognitive Load: 8/10 — The answer is well-organized and easy to understand.\nOVERALL: 8/10 — The answer is accurate and includes useful caveats, but citation verification is limited.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 0 | 0 | 0 | 5 | 8 | 3 |\n| B1: LLM + Search | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2: LLM + Search + Reflection | 10 | 8 | 5 | 9 | 9 | 8 |\n| MA: Multi-Agent | 10 | 7 | 6 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer provides no factual information about the mission.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The model accurately states its low confidence due to lack of information.\nCognitive Load: 5/10 — The answer is clear and easy to follow, but lacks content.\nOVERALL: 2/10 — The response is clear but lacks any factual content or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 8/10 — Citations are mentioned but not detailed, limiting verification.\nCoverage: 5/10 — The answer focuses solely on the requested details without additional context.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — Accurate and clear, but citation details are lacking.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 8/10 — Citations are mentioned but not detailed, limiting verification.\nCoverage: 6/10 — The answer acknowledges the limitation of a single source but lacks broader context.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — Accurate and clear, with a slight improvement in coverage acknowledgment.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately provides all requested details.\nCitation Quality: 7/10 — Citations are mentioned but lack a source URL, limiting verification.\nCoverage: 6/10 — The answer includes caveats about source limitations but lacks broader context.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — Accurate and clear, with acknowledgment of citation limitations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2 | 10 | 8 | 6 | 9 | 9 | 8 |\n| MA | 10 | 7 | 6 | 9 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 8, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } } }, "gpt-4o_q3": { "id": "fresh-20260623-003", "question": "In NASA's June 22, 2026 media advisory, which country is scheduled to sign the Artemis Accords, at what time/date, who will host, and what signer number will it become?", "answer_key": "Botswana; 9:30 a.m. EDT Thursday, June 25, 2026; hosted by NASA Deputy Administrator Matt Anderson; Botswana will be the 68th country to sign.", "source": "https://www.nasa.gov/news-release/nasa-invites-media-to-botswana-artemis-accords-signing-ceremony/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide information on NASA's June 22, 2026 media advisory or details about a country scheduled to sign the Artemis Accords on that date. My training only includes data up to October 202", "llm_calls": 1, "tool_calls": 0, "tokens_in": 128, "tokens_out": 116, "duration_ms": 1904, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In NASA's June 22, 2026 media advisory, the Republic of Botswana is scheduled to sign the Artemis Accords. The signing ceremony will take place at 9:30 a.m. EDT on Thursday, June 25, at NASA Headquart", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1417, "tokens_out": 175, "duration_ms": 2455, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In NASA's June 22, 2026 media advisory, the Republic of Botswana is scheduled to sign the Artemis Accords. The signing ceremony will take place at 9:30 a.m. EDT on Thursday, June 25, at NASA Headquart", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4583, "tokens_out": 556, "duration_ms": 7442, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe Republic of Botswana is scheduled to sign the Artemis Accords. The signing ceremony will take place at 9:30 a.m. EDT on Thursday, June 25, 2026. NASA Deputy Administrator Matt Anderson ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4304, "tokens_out": 1087, "duration_ms": 62261, "llm_duration_ms": 62237, "tool_duration_ms": 48933, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the event.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The answer correctly states low confidence due to lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks content.\n\nOVERALL: 2/10 — The response is clear but fails to provide any relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer is mostly accurate but includes additional details not in the authoritative answer.\nCitation Quality: 5/10 — The citation is mentioned but not verified with a real source.\nCoverage: 8/10 — The answer covers multiple aspects of the event.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\n\nOVERALL: 8/10 — The answer is comprehensive and mostly accurate but lacks verified citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer is mostly accurate but includes additional details not in the authoritative answer.\nCitation Quality: 5/10 — The citation is mentioned but not verified with a real source.\nCoverage: 8/10 — The answer covers multiple aspects of the event.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\n\nOVERALL: 8/10 — The answer is comprehensive and mostly accurate but lacks verified citations.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer is mostly accurate but lacks some details.\nCitation Quality: 4/10 — The citation is mentioned but not verified with a real source.\nCoverage: 7/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 8/10 — The answer is clear and concise.\n\nOVERALL: 7/10 — The answer is accurate and clear but lacks verified citations and additional perspectives.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM| 0 | 0 | 0 | 5 | 5 | 2 |\n| B1: LLM + Search| 9 | 5 | 8 | 8 | 9 | 8 |\n| B2: LLM + Search + Reflection| 9 | 5 | 8 | 8 | 9 | 8 |\n| MA: Multi-Agent| 9 | 4 | 7 | 8 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the event.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The answer correctly states low confidence due to lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The response is clear but fails to provide any relevant information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer is mostly accurate but includes additional, unsupported details.\nCitation Quality: 5/10 — A citation is mentioned but not properly provided or verified.\nCoverage: 8/10 — The answer covers the main points but includes unnecessary details.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is mostly accurate and well-presented but lacks proper citation.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer is mostly accurate but includes additional, unsupported details.\nCitation Quality: 5/10 — A citation is mentioned but not properly provided or verified.\nCoverage: 8/10 — The answer covers the main points but includes unnecessary details.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is mostly accurate and well-presented but lacks proper citation.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer is mostly accurate but lacks some details.\nCitation Quality: 4/10 — A citation is mentioned but not properly provided or verified.\nCoverage: 7/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 8/10 — The confidence is high, which is mostly justified by the accuracy.\nCognitive Load: 8/10 — The answer is clear and concise.\nOVERALL: 7/10 — The answer is accurate and concise but lacks proper citation and additional perspectives.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 9 | 5 | 8 | 8 | 8 | 8 |\n| B2 | 9 | 5 | 8 | 8 | 8 | 8 |\n| MA | 9 | 4 | 7 | 8 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 4, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 4, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } } }, "gpt-4o_q4": { "id": "fresh-20260623-004", "question": "According to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, which Webb instrument was used, what unusual chemical measurements were highlighted, and where/when was the paper published?", "answer_key": "Instrument: NIRSpec / Near-Infrared Spectrograph. Measurements: carbon and deuterium/heavy-hydrogen chemical ratios unlike solar-system comets. Paper published June 22 in Nature.", "source": "https://science.nasa.gov/missions/webb/nasas-webb-finds-clues-to-ancient-distant-origin-of-comet-3i-atlas/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have access to specific NASA stories or publications from June 22, 2026, as my training only includes data up to October 2023. Therefore, I cannot provide details on which Webb ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 131, "tokens_out": 119, "duration_ms": 1790, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "- **Webb Instrument Used**: The NIRSpec (Near-Infrared Spectrograph) instrument on NASA's James Webb Space Telescope was used [1].\n- **Unusual Chemical Measurements Highlighted**: The Webb telescope c", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1645, "tokens_out": 186, "duration_ms": 3207, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "- **Webb Instrument Used**: The NIRSpec (Near-Infrared Spectrograph) instrument on NASA’s James Webb Space Telescope was used to study comet 3I/ATLAS [1].\n\n- **Unusual Chemical Measurements Highlighte", "llm_calls": 4, "tool_calls": 8, "tokens_in": 5375, "tokens_out": 635, "duration_ms": 10254, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nAccording to NASA's June 22, 2026 Webb story on comet 3I/ATLAS, the Webb instrument used was the NIRSpec (Near-Infrared Spectrograph). The unusual chemical measurements highlighted were the", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4441, "tokens_out": 1208, "duration_ms": 63071, "llm_duration_ms": 63046, "tool_duration_ms": 47769, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the question.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks content.\nOVERALL: 2/10 — The answer fails to provide any relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 8/10 — Citations are relevant but not directly verifiable.\nCoverage: 8/10 — The answer covers all necessary aspects but lacks depth in perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately provides all required details.\nCitation Quality: 8/10 — Citations are relevant but not directly verifiable.\nCoverage: 8/10 — The answer covers all necessary aspects but lacks depth in perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-organized.\nOVERALL: 9/10 — A thorough and precise answer with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the instrument, measurements, and publication details.\nCitation Quality: 7/10 — Citations are mentioned but not directly accessible or verifiable.\nCoverage: 8/10 — The answer covers all necessary aspects but lacks depth in perspectives.\nCalibration: 8/10 — The confidence is high but slightly undermined by citation issues.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 8/10 — A solid answer with accurate information but citation accessibility issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 8 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 8 | 8 | 9 | 9 | 9 |\n| MA | 10 | 7 | 8 | 8 | 9 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the question.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The model correctly states its limitations, but the confidence is irrelevant as no information is provided.\nCognitive Load: 5/10 — The answer is clear but lacks content.\nOVERALL: 2/10 — The response is clear about its limitations but fails to provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 5/10 — The answer provides the necessary information but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and clear, though it could benefit from more detailed citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is consistent with the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 5/10 — The answer provides the necessary information but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and clear, though it could benefit from more detailed citations.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 6/10 — The citation is mentioned but lacks a direct link or detailed reference.\nCoverage: 5/10 — The answer provides the necessary information but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The confidence is high, but the lack of direct source access is noted.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate and clear, though it could benefit from more detailed citations and source access.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 8 | 5 | 9 | 9 | 8 |\n| B2 | 10 | 8 | 5 | 9 | 9 | 8 |\n| MA | 10 | 6 | 5 | 8 | 9 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 6, "Coverage": 5, "Calibration": 8, "Cognitive Load": 9, "OVERALL": 8 } } }, "gpt-4o_q5": { "id": "fresh-20260623-005", "question": "In NASA's June 22, 2026 Chandra image article, where is the possible supernova remnant located and what would make it notable if confirmed?", "answer_key": "It is in the middle/central region of the Milky Way. If confirmed, it would be one of the closest supernova remnants ever discovered to the supermassive black hole at the Galactic Center.", "source": "https://www.nasa.gov/image-article/nasas-chandra-finds-possible-supernova-remnant/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide details from a specific NASA article dated June 22, 2026, as my training only includes information up to October 2023. Therefore, I cannot access or summarize content from future", "llm_calls": 1, "tool_calls": 0, "tokens_in": 120, "tokens_out": 134, "duration_ms": 3068, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The possible supernova remnant is located in the middle of the Milky Way galaxy. If confirmed, it would be notable for being one of the closest supernova remnants ever discovered to the supermassive b", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1491, "tokens_out": 122, "duration_ms": 2984, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The possible supernova remnant is located in the middle of the Milky Way galaxy. If confirmed, it would be notable because it would be one of the closest supernova remnants ever discovered to the supe", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4710, "tokens_out": 452, "duration_ms": 7631, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe possible supernova remnant is located in the middle of the Milky Way galaxy. If confirmed, it would be notable for being one of the closest supernova remnants ever discovered to the sup", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3902, "tokens_out": 922, "duration_ms": 90864, "llm_duration_ms": 90843, "tool_duration_ms": 76676, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the supernova remnant.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is appropriately low for future content, but it doesn't match the lack of an answer.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 2/10 — The response fails to address the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — A citation is mentioned but not properly detailed or verified.\nCoverage: 5/10 — The answer provides the basic information but lacks depth or counterarguments.\nCalibration: 8/10 — The confidence is high and justified by the correct information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — The answer is accurate and clear but lacks citation detail and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 5/10 — A citation is mentioned but not properly detailed or verified.\nCoverage: 5/10 — The answer provides the necessary information but lacks additional perspectives.\nCalibration: 8/10 — The confidence is high and justified by the correct information provided.\nCognitive Load: 8/10 — The answer is straightforward and easy to understand.\nOVERALL: 7/10 — The answer is accurate and well-structured but lacks citation detail and depth.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 6/10 — A citation is provided but lacks a direct link or detailed reference.\nCoverage: 6/10 — The answer includes a caveat, adding some depth to the response.\nCalibration: 9/10 — The confidence is high and well-supported by the information provided.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The answer is accurate, clear, and includes a caveat, but the citation could be more detailed.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 5 | 5 | 8 | 8 | 7 |\n| B2 | 10 | 5 | 5 | 8 | 8 | 7 |\n| MA | 10 | 6 | 6 | 9 | 9 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the supernova remnant.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 1/10 — The answer suggests checking NASA's website but does not address the question directly.\nCalibration: 2/10 — The model correctly states its limitations but offers no useful information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The response fails to address the question but is straightforward about its limitations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — A citation is mentioned but not properly formatted or verifiable.\nCoverage: 3/10 — The answer is accurate but lacks multiple perspectives or counterarguments.\nCalibration: 8/10 — The confidence level is appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — The response is accurate and clear but lacks citation quality and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — A citation is mentioned but not properly formatted or verifiable.\nCoverage: 3/10 — The answer is accurate but lacks multiple perspectives or counterarguments.\nCalibration: 8/10 — The confidence level is appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — The response is accurate and clear but lacks citation quality and depth.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the location and significance of the supernova remnant.\nCitation Quality: 5/10 — A citation is mentioned but not properly formatted or verifiable.\nCoverage: 4/10 — The answer includes a caveat but lacks multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — The response is accurate, clear, and slightly more comprehensive due to the caveat.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 1 | 2 | 5 | 2 |\n| B1 | 10 | 5 | 3 | 8 | 8 | 7 |\n| B2 | 10 | 5 | 3 | 8 | 8 | 7 |\n| MA | 10 | 5 | 4 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 6, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 1, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 3, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 3, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 4, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q6": { "id": "fresh-20260623-006", "question": "For NASA's US Spacewalk 95 announcement, what task will astronauts perform, when is the spacewalk scheduled to begin, and who are the three preview briefing participants listed?", "answer_key": "Task: replace a wrist joint on the ISS Canadarm2 robotic arm. Start: approximately 8:35 a.m. EDT Tuesday, June 30, 2026. Briefing participants: Bill Spetch, Fiona Antkowiak, and Jason Dyer.", "source": "https://www.nasa.gov/news-release/nasa-to-cover-us-spacewalk-95-host-preview-news-conference/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I cannot provide specific details about NASA's US Spacewalk 95 announcement, such as the tasks astronauts will perform, the scheduled start time, or the names of the preview briefing participants, as ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 122, "tokens_out": 102, "duration_ms": 2594, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "For NASA's US Spacewalk 95, the astronauts will perform the task of replacing a wrist joint on the International Space Station's Canadarm2 robotic arm. The spacewalk is scheduled to begin at approxima", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1331, "tokens_out": 152, "duration_ms": 2613, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "For NASA's US Spacewalk 95 announcement, the astronauts will perform the task of replacing a wrist joint on the International Space Station's Canadarm2 robotic arm. The spacewalk is scheduled to begin", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4354, "tokens_out": 563, "duration_ms": 6617, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nNASA's US Spacewalk 95 will involve astronauts replacing a wrist joint on the International Space Station's Canadarm2 robotic arm. The spacewalk is scheduled to begin at approximately 8:35 ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4427, "tokens_out": 1258, "duration_ms": 83546, "llm_duration_ms": 83524, "tool_duration_ms": 68558, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer provides no factual information about the spacewalk.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The low confidence is appropriate given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The response is clear but fails to provide any relevant information.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer is mostly accurate but omits the year 2026.\nCitation Quality: 8/10 — Citations are relevant but not directly verifiable.\nCoverage: 7/10 — The answer covers the main points but lacks depth.\nCalibration: 9/10 — The high confidence is mostly justified by the accuracy.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — A strong response with minor omissions.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is fully accurate and matches the authoritative key.\nCitation Quality: 8/10 — Citations are relevant but not directly verifiable.\nCoverage: 8/10 — The answer covers the main points and provides context.\nCalibration: 10/10 — The high confidence is justified by the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-organized.\nOVERALL: 9/10 — An excellent response with comprehensive coverage.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer is fully accurate and matches the authoritative key.\nCitation Quality: 9/10 — Citations are relevant and well-integrated.\nCoverage: 9/10 — The answer includes caveats and context.\nCalibration: 10/10 — The high confidence is justified by the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A thorough and well-supported response.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM| 0 | 0 | 0 | 5 | 5 | 2 |\n| B1: LLM + Search| 9 | 8 | 7 | 9 | 9 | 8 |\n| B2: LLM + Search + Reflection| 10 | 8 | 8 | 10 | 9 | 9 |\n| MA: Multi-Agent| 10 | 9 | 9 | 10 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer provides no factual information about the spacewalk.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The low confidence is appropriate given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The answer fails to provide any relevant information or citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the task, time, and participants but omits the year.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link or detailed source information.\nCoverage: 5/10 — The answer focuses on the main points but does not explore additional perspectives.\nCalibration: 8/10 — The high confidence is mostly justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 7/10 — A mostly accurate and clear answer with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies all details, including the year.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct link or detailed source information.\nCoverage: 5/10 — The answer covers the main points but does not explore additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and complete information.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 8/10 — An accurate and clear answer with minor citation issues.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies all details, including the year.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but lacks a direct link.\nCoverage: 6/10 — The answer includes a caveat but does not explore additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and complete information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate answer with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 8 | 7 | 5 | 8 | 8 | 7 |\n| B2 | 10 | 7 | 5 | 9 | 8 | 8 |\n| MA | 10 | 8 | 6 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 7, "Coverage": 5, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 6, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q7": { "id": "fresh-20260623-007", "question": "What real-world data-collection bottleneck does the June 22, 2026 arXiv paper 'AutoDex' claim to address, and what loop must run without human intervention?", "answer_key": "It addresses scalable real-world dexterous grasping data collection: teleoperation is slow/operator-biased and simulation cannot certify contact validity. The loop is perception, execution, labeling, and reset running without human intervention.", "source": "https://arxiv.org/abs/2606.23689v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I cannot provide information on a paper titled \"AutoDex\" from June 22, 2026, as it is beyond my current knowledge base, which includes data up to October 2023. Additionally, I cannot ac", "llm_calls": 1, "tool_calls": 0, "tokens_in": 125, "tokens_out": 106, "duration_ms": 1805, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The June 22, 2026 arXiv paper \"AutoDex\" claims to address the real-world data-collection bottleneck of obtaining robust dexterous grasping data. This data is difficult to collect at scale because tele", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1514, "tokens_out": 199, "duration_ms": 4757, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The June 22, 2026 arXiv paper 'AutoDex' addresses the real-world data-collection bottleneck of obtaining robust dexterous grasping data at scale. The paper identifies that teleoperation, while yieldin", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4938, "tokens_out": 611, "duration_ms": 7514, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe June 22, 2026 arXiv paper 'AutoDex' addresses the bottleneck of collecting real-world data for dexterous grasping, which is challenging due to the slow and operator-biased nature of tel", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4329, "tokens_out": 1321, "duration_ms": 85159, "llm_duration_ms": 85141, "tool_duration_ms": 62537, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 0/10 — The confidence is low, which matches the lack of information, but this is not a positive aspect.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question in any meaningful way.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, but lacks a direct link.\nCoverage: 9/10 — The answer covers the main points and counterarguments effectively.\nCalibration: 9/10 — The confidence is high and justified by the quality of the answer.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is factually correct and aligns with the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and includes a direct link.\nCoverage: 10/10 — The answer thoroughly covers the necessary perspectives and counterarguments.\nCalibration: 10/10 — The high confidence is well-matched to the quality of the answer.\nCognitive Load: 10/10 — The answer is exceptionally clear and well-organized.\nOVERALL: 10/10 — An exemplary response with perfect alignment to the authoritative answer.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer is accurate and consistent with the authoritative answer key.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link.\nCoverage: 8/10 — The answer is comprehensive but does not mention potential challenges.\nCalibration: 9/10 — The high confidence is mostly justified, though some caveats are noted.\nCognitive Load: 9/10 — The answer is clear and easy to follow.\nOVERALL: 9/10 — A strong response with minor areas for improvement in citation and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 0 | 5 | 1 |\n| B1 | 10 | 8 | 9 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 10 | 10 | 10 | 10 |\n| MA | 10 | 8 | 8 | 9 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any relevant information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 0/10 — The confidence is low, but the answer does not attempt to answer the question.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 1/10 — The answer fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the claims made in the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it lacks a direct link.\nCoverage: 9/10 — The answer covers the main points and perspectives mentioned in the authoritative answer.\nCalibration: 9/10 — The confidence is high and justified by the accuracy of the answer.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer is fully aligned with the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and includes a direct link.\nCoverage: 10/10 — The answer thoroughly covers the necessary perspectives and details.\nCalibration: 10/10 — The high confidence is appropriate given the accuracy and completeness.\nCognitive Load: 10/10 — The answer is clear, concise, and well-organized.\nOVERALL: 10/10 — An exemplary response that meets all criteria perfectly.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the bottleneck and loop as per the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant but lacks a direct link.\nCoverage: 8/10 — The answer is comprehensive but does not mention potential challenges or limitations.\nCalibration: 9/10 — The confidence is high and mostly justified, though some caveats are noted.\nCognitive Load: 9/10 — The answer is clear and well-structured, with a minor caveat section.\nOVERALL: 9/10 — A strong response with minor areas for improvement in coverage and citation.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 0 | 5 | 1 |\n| B1 | 10 | 9 | 9 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 10 | 10 | 10 | 10 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 0, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 10, "Calibration": 10, "Cognitive Load": 10, "OVERALL": 10 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 0, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 10, "Calibration": 10, "Cognitive Load": 10, "OVERALL": 10 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q8": { "id": "fresh-20260623-008", "question": "In 'Randomized YaRN Improves Length Generalization for Long-Context Reasoning,' what three components are combined in the proposed training method?", "answer_key": "YaRN-based positional extrapolation, randomized positional encoding, and a length curriculum.", "source": "https://arxiv.org/abs/2606.23687v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have access to specific papers or their contents, including \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning.\" Therefore, I cannot provide the details ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 116, "tokens_out": 136, "duration_ms": 1556, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In the paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning,\" the proposed training method combines three components: YaRN-based positional extrapolation, randomized positi", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1362, "tokens_out": 116, "duration_ms": 2008, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In the paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning,\" the proposed training method combines three components: YaRN-based positional extrapolation, randomized positi", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4614, "tokens_out": 521, "duration_ms": 7885, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nIn the paper \"Randomized YaRN Improves Length Generalization for Long-Context Reasoning,\" the proposed training method combines three components: YaRN-based positional extrapolation, random", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3764, "tokens_out": 822, "duration_ms": 76019, "llm_duration_ms": 76002, "tool_duration_ms": 63327, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is appropriately low, but the answer is not useful.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question and lacks any factual content.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the three components from the paper.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer directly addresses the question but does not explore multiple perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the three components from the paper.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer directly addresses the question but does not explore multiple perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the three components from the paper.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or proper formatting.\nCoverage: 8/10 — The answer directly addresses the question but does not explore multiple perspectives.\nCalibration: 8/10 — The confidence is high, but the caveat about source accessibility slightly undermines it.\nCognitive Load: 8/10 — The answer is clear but slightly cluttered with unnecessary sections.\nOVERALL: 8/10 — The answer is accurate and well-structured but could improve citation formatting.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 8 | 9 | 9 | 9 |\n| MA | 10 | 8 | 8 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The answer accurately reflects its low confidence in providing specific details.\nCognitive Load: 5/10 — The answer is clear and easy to follow, but lacks content.\nOVERALL: 2/10 — The response is clear but fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly identifies the three components from the paper.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 5/10 — The answer provides the necessary components but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — The response is accurate, well-cited, and easy to understand, though it could include more perspectives.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the three components from the paper.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 5/10 — The answer provides the necessary components but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 9/10 — The answer is concise and easy to follow.\nOVERALL: 9/10 — The response is accurate, well-cited, and easy to understand, though it could include more perspectives.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer correctly identifies the three components from the paper.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or confirmation of access.\nCoverage: 5/10 — The answer provides the necessary components but lacks additional perspectives or counterarguments.\nCalibration: 8/10 — The confidence is high, but the caveat about source access slightly undermines it.\nCognitive Load: 8/10 — The answer is clear but slightly less concise due to the caveats section.\nOVERALL: 8/10 — The response is accurate and well-structured, though it could improve citation clarity and include more perspectives.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 10 | 5 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 5 | 9 | 9 | 9 |\n| MA | 10 | 8 | 5 | 8 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 5, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q9": { "id": "fresh-20260623-009", "question": "What stop-and-go simplification does 'CoorDex' criticize, and what control formulation does it introduce?", "answer_key": "It criticizes walking to an object, stopping to manipulate it, then resuming locomotion, often with low-DoF open-close end effectors. It introduces coordinated latent residual control for high-DoF dexterous loco-manipulation on the move.", "source": "https://arxiv.org/abs/2606.23680v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I am not familiar with \"CoorDex\" or its criticisms and formulations. It is possible that this is a specific topic or a niche subject not widely covered in the sources I have access to. ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 109, "tokens_out": 94, "duration_ms": 1239, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "'CoorDex' criticizes the stop-and-go simplification in humanoid loco-manipulation, where the process is divided into distinct phases: walking to an object, stopping to manipulate it, and then resuming", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1382, "tokens_out": 212, "duration_ms": 3460, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "'CoorDex' criticizes the simplification of humanoid loco-manipulation into a stop-and-go process, where the robot walks to an object, stops to manipulate it, and then resumes locomotion. This approach", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4569, "tokens_out": 654, "duration_ms": 10379, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nCoorDex criticizes the stop-and-go simplification of humanoid loco-manipulation, which involves walking to an object, stopping to manipulate it, and then resuming locomotion. Instead, CoorD", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4064, "tokens_out": 1075, "duration_ms": 76869, "llm_duration_ms": 76854, "tool_duration_ms": 63519, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about CoorDex.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it still fails to provide any useful content.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question in any meaningful way.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately describes CoorDex's criticism and control formulation.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — The answer covers the main points but lacks counterarguments or additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and well-supported answer.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and directly supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 9/10 — The answer is clear and easy to follow.\nOVERALL: 9/10 — A thorough and well-supported answer.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately describes CoorDex's criticism and control formulation.\nCitation Quality: 9/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 7/10 — The answer mentions a caveat but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 8/10 — The answer is clear but could be more detailed in its structure.\nOVERALL: 9/10 — A solid and well-supported answer with minor room for improvement.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 8 | 9 | 9 | 9 |\n| MA | 10 | 9 | 7 | 9 | 8 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about CoorDex.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The model correctly states its low confidence, but the lack of information is a significant issue.\nCognitive Load: 5/10 — The answer is easy to follow but lacks content.\nOVERALL: 1/10 — The response fails to address the question in any meaningful way.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately describes CoorDex's criticism and control formulation.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — The answer covers the main points but lacks multiple perspectives or counterarguments.\nCalibration: 9/10 — The confidence is appropriately high given the accuracy and citation.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is accurate and directly supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy and citation.\nCognitive Load: 9/10 — The answer is clear and well-organized.\nOVERALL: 9/10 — A precise and well-cited response.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately describes CoorDex's criticism and control formulation.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct link or detailed reference.\nCoverage: 7/10 — The answer provides a caveat but lacks broader perspectives or counterarguments.\nCalibration: 9/10 — The confidence is appropriate given the accuracy and citation.\nCognitive Load: 9/10 — The answer is clear and easy to follow.\nOVERALL: 9/10 — A clear and accurate response with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 8 | 9 | 9 | 9 |\n| MA | 10 | 8 | 7 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q10": { "id": "fresh-20260623-010", "question": "What problem with modern text-to-image models motivates 'Semantic Browsing,' and what user capability does the method aim to provide?", "answer_key": "Strict prompt adherence can collapse samples into a single visual interpretation, reducing meaningful diversity. Semantic Browsing aims to let users navigate controlled, structured diversity through meaningful design choices.", "source": "https://arxiv.org/abs/2606.23679v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "Modern text-to-image models, such as DALL-E and Stable Diffusion, often face challenges related to the specificity and control over the generated images. These models can produce high-quality images f", "llm_calls": 1, "tool_calls": 0, "tokens_in": 112, "tokens_out": 320, "duration_ms": 4183, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The problem with modern text-to-image models that motivates 'Semantic Browsing' is their strict adherence to prompts, which results in a lack of diversity. Generated samples tend to collapse into a si", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1227, "tokens_out": 191, "duration_ms": 3067, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The problem with modern text-to-image models that motivates 'Semantic Browsing' is their tendency to collapse into a single visual interpretation, which sacrifices diversity for visual fidelity and pr", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4167, "tokens_out": 683, "duration_ms": 8513, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe problem with modern text-to-image models that motivates 'Semantic Browsing' is their tendency to collapse into a single visual interpretation, sacrificing diversity for strict adherence", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3764, "tokens_out": 981, "duration_ms": 116245, "llm_duration_ms": 116225, "tool_duration_ms": 103371, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the lack of control and specificity in text-to-image models but misses the key issue of collapsing into a single interpretation.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 5/10 — The answer discusses user interaction and control but lacks depth on the diversity issue.\nCalibration: 4/10 — The confidence level is appropriately medium, but the lack of citations undermines the explanation.\nCognitive Load: 7/10 — The answer is well-structured and easy to follow, though it lacks depth.\nOVERALL: 4/10 — The answer is clear but lacks critical details and citations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer accurately identifies the problem of collapsing into a single interpretation and the aim of Semantic Browsing.\nCitation Quality: 2/10 — Citations are mentioned but not provided, reducing credibility.\nCoverage: 7/10 — The answer covers the main issues and user capabilities but lacks counterarguments.\nCalibration: 7/10 — The confidence is high, which is mostly justified by the accurate description, though citations are missing.\nCognitive Load: 8/10 — The answer is concise and well-structured.\nOVERALL: 6/10 — A mostly accurate and clear answer, but the lack of actual citations is a significant drawback.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately describes the problem and the aim of Semantic Browsing.\nCitation Quality: 9/10 — A relevant citation is provided, supporting the claims.\nCoverage: 8/10 — The answer covers the main issues and user capabilities well, though it could include more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate content and citation.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and well-supported answer with strong citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the problem and the aim of Semantic Browsing.\nCitation Quality: 8/10 — A citation is provided, but the URL is not complete.\nCoverage: 8/10 — The answer covers the main issues and user capabilities well, though it could include more perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate content and citation.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — A comprehensive and well-supported answer with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 0 | 5 | 4 | 7 | 4 |\n| B1 | 8 | 2 | 7 | 7 | 8 | 6 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 8 | 8 | 9 | 9 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the lack of control and specificity in text-to-image models but does not mention the collapse into a single visual interpretation.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 5/10 — The answer discusses user interaction and control but lacks mention of structured diversity and meaningful design choices.\nCalibration: 5/10 — The confidence level is appropriately medium given the lack of citations and partial coverage.\nCognitive Load: 7/10 — The answer is generally clear and well-structured but could be more concise.\nOVERALL: 5/10 — The answer partially addresses the question but lacks citations and full coverage of the key points.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer accurately identifies the problem of collapsing into a single interpretation and the aim of Semantic Browsing.\nCitation Quality: 2/10 — Citations are mentioned but not provided, reducing their usefulness.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing user capabilities.\nCalibration: 8/10 — The confidence level is high, which is mostly justified by the accurate content.\nCognitive Load: 8/10 — The answer is clear and concise, making it easy to follow.\nOVERALL: 7/10 — A strong answer with accurate content but lacking in citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately reflects the authoritative answer key, including the collapse into a single interpretation and structured diversity.\nCitation Quality: 9/10 — A direct citation is provided, supporting the claims effectively.\nCoverage: 8/10 — The answer covers the main points well but could include more on user capabilities.\nCalibration: 9/10 — The high confidence is justified by the accurate content and citation.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and well-supported answer with excellent citation quality.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the problem and the aim of Semantic Browsing.\nCitation Quality: 8/10 — A citation is provided, but the source URL is not fully detailed.\nCoverage: 7/10 — The answer covers the main points but lacks depth in discussing user capabilities and examples.\nCalibration: 9/10 — The high confidence is justified by the accurate content and citation.\nCognitive Load: 8/10 — The answer is clear but could be more detailed in its explanation.\nOVERALL: 8/10 — A strong answer with good citation quality but lacking in depth and detail.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 0 | 5 | 5 | 7 | 5 |\n| B1 | 8 | 2 | 7 | 8 | 8 | 7 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 2, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 5, "Calibration": 5, "Cognitive Load": 7, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 2, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q11": { "id": "fresh-20260623-011", "question": "According to the AIR arXiv abstract, what limitation of prior interleaved-reasoning/tool-use work does AIR target?", "answer_key": "Prior work focuses mainly on predefined heuristic visual manipulations for vision-perception tasks and is inherently unable to address numerical computation problems; AIR targets adaptive interleaved reasoning with code in MLLMs.", "source": "https://arxiv.org/abs/2606.23678v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I can't provide specific details from the AIR arXiv abstract as it requires access to the document, which I don't have. However, I can provide a general understanding of the limitations in prior inter", "llm_calls": 1, "tool_calls": 0, "tokens_in": 113, "tokens_out": 239, "duration_ms": 2894, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "According to the AIR arXiv abstract, the limitation of prior interleaved-reasoning/tool-use work that AIR targets is the reliance on predefined heuristics for visual manipulation, which makes these ap", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1510, "tokens_out": 174, "duration_ms": 2836, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "According to the AIR arXiv abstract, the limitation of prior interleaved-reasoning/tool-use work that AIR targets is the exclusive focus on visual operations, which makes them inherently incapable of ", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4809, "tokens_out": 555, "duration_ms": 7802, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe limitation of prior interleaved-reasoning/tool-use work that AIR targets is the reliance on predefined heuristics for visual manipulation, which makes them inherently incapable of addre", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3533, "tokens_out": 729, "duration_ms": 54476, "llm_duration_ms": 54455, "tool_duration_ms": 45241, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer provides a general overview but does not accurately identify the specific limitation targeted by AIR.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer discusses general limitations but lacks specific perspectives or counterarguments related to AIR.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the lack of specific information, but the answer is still off-target.\nCognitive Load: 6/10 — The answer is easy to follow but lacks depth and specificity.\nOVERALL: 3/10 — The response is too generic and lacks the necessary detail and citations to be useful.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the limitation related to predefined heuristics and numerical computation.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 7/10 — The answer addresses the main limitation but lacks discussion of counterarguments or additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is concise and easy to understand.\nOVERALL: 8/10 — A strong response with accurate information and good citation, though it could benefit from broader coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the limitation and clarifies the focus on numerical computation.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 8/10 — The answer provides a clear explanation and addresses the limitation well, though it could include more perspectives.\nCalibration: 9/10 — The confidence is high and well-supported by the accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate response with strong citation and clarity.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the limitation but lacks the nuance of the authoritative answer.\nCitation Quality: 7/10 — The citation is relevant but lacks a direct URL, limiting verification.\nCoverage: 6/10 — The answer focuses on the main limitation but does not explore additional perspectives or counterarguments.\nCalibration: 8/10 — The confidence is high and mostly justified, though the lack of a direct URL is a minor issue.\nCognitive Load: 8/10 — The answer is clear and easy to understand.\nOVERALL: 7/10 — A good response with accurate information, but it could improve in citation detail and coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 4 | 6 | 3 |\n| B1 | 8 | 9 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 8 | 7 | 6 | 8 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer provides a general overview of limitations in AI but does not specifically address the limitation targeted by AIR.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer discusses general limitations but fails to address the specific limitation mentioned in the authoritative answer.\nCalibration: 4/10 — The confidence is medium, which is appropriate given the lack of specific information, but the answer is not aligned with the authoritative source.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth and specificity.\nOVERALL: 3/10 — The answer is too general and lacks the necessary detail and citations to be useful.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the limitation targeted by AIR but misses the aspect of predefined heuristic visual manipulations.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 7/10 — The answer addresses the main limitation but lacks discussion on adaptive interleaved reasoning.\nCalibration: 9/10 — The confidence is high, which is appropriate given the accuracy of the information provided.\nCognitive Load: 8/10 — The answer is concise and easy to understand.\nOVERALL: 8/10 — A strong answer with accurate information and good citation quality, though slightly lacking in coverage.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer accurately identifies the limitation and the approach AIR takes to address it.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more detailed.\nCoverage: 9/10 — The answer covers both the limitation and the solution provided by AIR.\nCalibration: 9/10 — The confidence is high, which matches the quality of the answer.\nCognitive Load: 9/10 — The answer is well-structured and easy to follow.\nOVERALL: 9/10 — A comprehensive and accurate answer with strong citation quality and coverage.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the limitation but does not mention the adaptive interleaved reasoning aspect.\nCitation Quality: 6/10 — The citation is relevant but lacks a direct URL, limiting verification.\nCoverage: 7/10 — The answer addresses the main limitation but lacks depth in discussing the solution.\nCalibration: 8/10 — The confidence is high, which is mostly appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is clear and easy to follow.\nOVERALL: 7/10 — A good answer with accurate information but lacking in citation detail and coverage depth.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 4 | 5 | 3 |\n| B1 | 8 | 9 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 9 | 9 | 9 | 9 |\n| MA | 8 | 6 | 7 | 8 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 7, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 6, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } } }, "gpt-4o_q12": { "id": "fresh-20260623-012", "question": "What open theoretical gap does 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' identify, and which optimizers does it contrast with AdamW?", "answer_key": "It identifies the lack of rigorous convergence theory for AdamW under heavy-tailed stochastic gradient noise in LLM pretraining. It contrasts AdamW with sign-based optimizers such as Lion and Muon, and with AdaGrad.", "source": "https://arxiv.org/abs/2606.23676v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The open theoretical gap identified by the problem \"Is AdamW Effective Under Heavy-Tailed Noise?\" pertains to understanding the performance of the AdamW optimizer in the presence of heavy-tailed noise", "llm_calls": 1, "tool_calls": 0, "tokens_in": 119, "tokens_out": 339, "duration_ms": 3273, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The open theoretical gap identified in \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" is the lack of a rigorous convergence theory for the AdamW optimizer under heavy-tailed noise condit", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1586, "tokens_out": 188, "duration_ms": 3264, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The open theoretical gap identified by \"Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?\" is the absence of a rigorous convergence theory for the AdamW optimizer under conditions of heavy-ta", "llm_calls": 4, "tool_calls": 9, "tokens_in": 5051, "tokens_out": 723, "duration_ms": 9215, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe open theoretical gap identified by 'Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?' is whether AdamW can converge under heavy-tailed noise conditions, which are common in la", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4991, "tokens_out": 1071, "duration_ms": 76483, "llm_duration_ms": 76461, "tool_duration_ms": 61502, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the gap related to AdamW and heavy-tailed noise but incorrectly includes SGD instead of the correct optimizers.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 5/10 — The answer discusses heavy-tailed noise and AdamW but fails to mention the correct contrasting optimizers.\nCalibration: 4/10 — The confidence is medium, which is appropriate given the lack of citations, but the answer is not fully accurate.\nCognitive Load: 7/10 — The answer is generally clear and easy to follow, though it lacks depth in coverage.\nOVERALL: 4/10 — The answer provides a basic understanding but lacks accuracy and citation support.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately identifies the theoretical gap and the correct optimizers.\nCitation Quality: 10/10 — The citation is real, relevant, and directly supports the claims.\nCoverage: 8/10 — The answer addresses the main points but could include more detail on the implications.\nCalibration: 9/10 — The confidence is high, which matches the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is concise and well-structured, making it easy to follow.\nOVERALL: 9/10 — A well-supported and accurate answer with excellent citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer correctly identifies the theoretical gap and the optimizers involved.\nCitation Quality: 10/10 — The citation is accurate and supports the claims made.\nCoverage: 9/10 — The answer provides a comprehensive view, including the context of LLM pretraining.\nCalibration: 9/10 — The high confidence is justified by the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is clear and logically structured.\nOVERALL: 9/10 — A thorough and well-cited answer that effectively covers the topic.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately identifies the theoretical gap and the correct optimizers.\nCitation Quality: 9/10 — The citation is relevant, but the source URL is not provided.\nCoverage: 9/10 — The answer covers the main points and includes additional context about convergence properties.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy and coverage.\nCognitive Load: 8/10 — The answer is clear but could be slightly more concise.\nOVERALL: 9/10 — A detailed and accurate answer with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 0 | 5 | 4 | 7 | 4 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 9 | 9 | 9 | 9 |\n| MA | 10 | 9 | 9 | 9 | 8 | 9 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 6/10 — The answer correctly identifies the gap related to AdamW and heavy-tailed noise but incorrectly contrasts it with SGD instead of the specified optimizers.\nCitation Quality: 2/10 — Citations are mentioned but not directly linked to the authoritative source, and one is fabricated.\nCoverage: 5/10 — The answer provides some context about heavy-tailed noise but lacks discussion on the specific optimizers mentioned in the authoritative answer.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the partial correctness, but the lack of specific citations undermines it.\nCognitive Load: 6/10 — The answer is generally clear but could be more concise and focused on the key points.\n\nOVERALL: 5/10 — The answer provides a basic understanding but lacks precision and correct citations.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately identifies the theoretical gap and correctly contrasts AdamW with the specified optimizers.\nCitation Quality: 9/10 — The citation is real, relevant, and directly supports the claims made.\nCoverage: 8/10 — The answer addresses the main points but could include more detail on the implications of the theoretical gap.\nCalibration: 9/10 — The high confidence is justified by the accuracy and citation quality.\nCognitive Load: 9/10 — The answer is concise, clear, and well-structured.\n\nOVERALL: 9/10 — A well-rounded answer with accurate information and strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer perfectly aligns with the authoritative answer key.\nCitation Quality: 9/10 — The citation is accurate and supports the claims, though it could include more context.\nCoverage: 9/10 — The answer covers the theoretical gap and contrasts with the specified optimizers effectively.\nCalibration: 10/10 — The high confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — The answer is clear and easy to follow, with a logical structure.\n\nOVERALL: 9/10 — An excellent answer that meets all criteria with minor room for additional context.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer correctly identifies the theoretical gap and contrasts AdamW with the specified optimizers.\nCitation Quality: 8/10 — The citation is relevant but lacks a direct URL, which slightly reduces its quality.\nCoverage: 8/10 — The answer provides a good overview but could delve deeper into the implications of the theoretical gap.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy and clarity of the answer.\nCognitive Load: 8/10 — The answer is well-structured but could be more concise.\n\nOVERALL: 8/10 — A strong answer with minor issues in citation presentation and depth of coverage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 6 | 2 | 5 | 4 | 6 | 5 |\n| B1 | 9 | 9 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 9 | 9 | 10 | 9 | 9 |\n| MA | 9 | 8 | 8 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 0, "Coverage": 5, "Calibration": 4, "Cognitive Load": 7, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 6, "Citation Quality": 2, "Coverage": 5, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 5 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q13": { "id": "fresh-20260623-013", "question": "What limitation in existing mental-health assessment approaches does 'PsyBridge' claim to address?", "answer_key": "Existing approaches rely on isolated screening instruments or data-driven models, lack interpretability and multi-dimensional integration, and focus on individual indicators like depression or anxiety rather than comprehensive explainable decision support.", "source": "https://arxiv.org/abs/2606.23673v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific citations or details about 'PsyBridge' as it appears to be a specialized or less widely documented topic that isn't covered in my training data up to October 2023. Howev", "llm_calls": 1, "tool_calls": 0, "tokens_in": 105, "tokens_out": 297, "duration_ms": 2747, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "PsyBridge claims to address the limitation in existing mental-health assessment approaches that rely on isolated screening instruments or data-driven models, which often lack interpretability and mult", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1314, "tokens_out": 145, "duration_ms": 3591, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "PsyBridge claims to address the limitation in existing mental-health assessment approaches that often rely on isolated screening instruments or data-driven models lacking interpretability and multi-di", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4300, "tokens_out": 671, "duration_ms": 8653, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nPsyBridge claims to address the limitation in existing mental-health assessment approaches that rely on isolated screening instruments or data-driven models, which often lack interpretabili", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3484, "tokens_out": 844, "duration_ms": 80525, "llm_duration_ms": 80505, "tool_duration_ms": 65444, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer provides general limitations of mental health assessments but does not specifically address PsyBridge.\nCitation Quality: 0/10 — No real or relevant citations are provided.\nCoverage: 2/10 — Offers a broad view of limitations but lacks specific focus on PsyBridge.\nCalibration: 2/10 — The confidence is appropriately low given the lack of specific information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth and specificity.\nOVERALL: 2/10 — The response is too generic and lacks specific information about PsyBridge.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Accurately identifies the limitations PsyBridge aims to address.\nCitation Quality: 7/10 — Provides a citation, but it is not verified as real or accessible.\nCoverage: 8/10 — Covers the main limitations and the proposed solution by PsyBridge.\nCalibration: 8/10 — Confidence is high and justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong answer with good coverage and clarity, though citation verification is lacking.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — Correctly identifies the limitations PsyBridge addresses.\nCitation Quality: 7/10 — Similar to B1, the citation is not verified.\nCoverage: 7/10 — Provides a good overview but lacks counterarguments or limitations of PsyBridge.\nCalibration: 8/10 — Confidence is high and mostly justified, though it lacks a balanced perspective.\nCognitive Load: 8/10 — The answer is coherent and easy to follow.\nOVERALL: 8/10 — A well-rounded answer with minor gaps in citation verification and coverage.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Accurately describes the limitations PsyBridge addresses.\nCitation Quality: 6/10 — Mentions a citation but does not provide a real or accessible source.\nCoverage: 7/10 — Covers the main points but lacks additional perspectives or recent developments.\nCalibration: 8/10 — Confidence is high and justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and concise.\nOVERALL: 8/10 — A solid answer with good clarity, though citation and coverage could be improved.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 2 | 5 | 2 |\n| B1 | 9 | 7 | 8 | 8 | 8 | 8 |\n| B2 | 9 | 7 | 7 | 8 | 8 | 8 |\n| MA | 9 | 6 | 7 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not specifically address the limitations PsyBridge claims to address.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 3/10 — General limitations of mental health assessments are discussed, but not in relation to PsyBridge.\nCalibration: 4/10 — The low confidence is appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth and specificity.\nOVERALL: 3/10 — The response is too general and lacks specific information about PsyBridge.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately reflects the limitations PsyBridge aims to address.\nCitation Quality: 7/10 — The citation is relevant but lacks a specific source URL.\nCoverage: 8/10 — The answer covers the main limitations but lacks counterarguments or additional perspectives.\nCalibration: 9/10 — The high confidence is justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 8/10 — A strong answer with accurate information but could benefit from more detailed citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the limitations PsyBridge addresses.\nCitation Quality: 7/10 — The citation is relevant but lacks a specific source URL.\nCoverage: 7/10 — The answer covers the main limitations but does not address potential criticisms or limitations of PsyBridge.\nCalibration: 9/10 — The high confidence is appropriate given the accurate information.\nCognitive Load: 8/10 — The answer is clear and concise.\nOVERALL: 8/10 — A well-rounded answer that could improve with more comprehensive coverage.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately identifies the limitations PsyBridge addresses.\nCitation Quality: 8/10 — The citation is relevant and includes a placeholder for the source URL.\nCoverage: 7/10 — The answer covers the main limitations but lacks additional perspectives or criticisms.\nCalibration: 9/10 — The high confidence is justified by the accurate synthesis.\nCognitive Load: 8/10 — The answer is well-structured and easy to understand.\nOVERALL: 8/10 — A solid answer with accurate information and good citation practice.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 3 | 4 | 5 | 3 |\n| B1 | 9 | 7 | 8 | 9 | 8 | 8 |\n| B2 | 9 | 7 | 7 | 9 | 8 | 8 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 3, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q14": { "id": "fresh-20260623-014", "question": "In the June 22, 2026 arXiv paper on bit manipulation puzzles, what is the task objective and what LLM failure mode do the authors say traditional methods induce?", "answer_key": "Objective: discover a hidden logical rule transforming input binary strings to outputs, then apply it to unseen inputs. Traditional methods force LLMs to simulate complex boolean logic/arithmetic, leading to hallucinations.", "source": "https://arxiv.org/abs/2606.23672v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide specific details about a paper from June 22, 2026, as my training only includes data up to October 2023. Therefore, I cannot access or summarize content from future publications,", "llm_calls": 1, "tool_calls": 0, "tokens_in": 124, "tokens_out": 133, "duration_ms": 3233, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The task objective in the June 22, 2026 arXiv paper on bit manipulation puzzles is to discover a hidden logical rule that transforms input binary strings to outputs and then apply this rule to unseen ", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1495, "tokens_out": 227, "duration_ms": 3605, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The task objective in the June 22, 2026 arXiv paper on bit manipulation puzzles is to discover a hidden logical rule that transforms input binary strings to outputs, and then apply this rule to unseen", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4884, "tokens_out": 626, "duration_ms": 9403, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\n1. The task objective is to discover a hidden logical rule that transforms input binary strings to outputs, and then apply this rule to unseen inputs [1].\n2. The LLM failure mode induced by", "llm_calls": 6, "tool_calls": 8, "tokens_in": 4038, "tokens_out": 971, "duration_ms": 88958, "llm_duration_ms": 88939, "tool_duration_ms": 75299, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The confidence is high, but the answer lacks any factual content.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The answer fails to address the question and lacks any factual basis.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is appropriately high given the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is appropriately high given the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is real and relevant, but lacks a direct link.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The confidence is appropriately high given the accurate content.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to understand.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0: Vanilla LLM | 0/10 | 0/10 | 0/10 | 2/10 | 5/10 | 1/10 |\n| B1: LLM + Search | 10/10 | 10/10 | 8/10 | 9/10 | 9/10 | 9/10 |\n| B2: LLM + Search + Reflection | 10/10 | 10/10 | 8/10 | 9/10 | 9/10 | 9/10 |\n| MA: Multi-Agent | 10/10 | 9/10 | 8/10 | 9/10 | 9/10 | 9/10 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the paper.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is high, but the answer admits its limitations.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The answer fails to address the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to follow.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 10/10 — The citation is real, relevant, and supports the claims.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to follow.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is real and relevant, but lacks full detail.\nCoverage: 8/10 — The answer covers the main points but lacks additional perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accuracy.\nCognitive Load: 9/10 — The answer is clear and well-structured.\nOVERALL: 9/10 — The answer is accurate, well-cited, and easy to follow.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 10 | 10 | 8 | 9 | 9 | 9 |\n| B2 | 10 | 10 | 8 | 9 | 9 | 9 |\n| MA | 10 | 9 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q15": { "id": "fresh-20260623-015", "question": "What did 'Can LLMs Reliably Self-Report Adversarial Prefills, and How?' find about models recognizing compromised outputs, and what average intent-claim rate is reported?", "answer_key": "Across ten open-weight instruction-tuned LLMs and four safety benchmarks, no model reliably recognized its own compromised outputs; models claimed intent on prefilled responses at an average rate of 27.3%.", "source": "https://arxiv.org/abs/2606.23671v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I don't have access to specific documents or papers titled \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" Therefore, I cannot provide a detailed answer or citations for ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 125, "tokens_out": 139, "duration_ms": 3535, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The paper \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that no model reliably recognizes its own compromised outputs. The models claimed intent on prefilled responses at an ave", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1598, "tokens_out": 155, "duration_ms": 3078, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that no model reliably recognizes its own compromised outputs when subjected to adversarial prefill attacks. The models c", "llm_calls": 4, "tool_calls": 9, "tokens_in": 6257, "tokens_out": 506, "duration_ms": 8628, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe study \"Can LLMs Reliably Self-Report Adversarial Prefills, and How?\" found that no model reliably recognizes its own compromised outputs. The models claimed intent on prefilled response", "llm_calls": 6, "tool_calls": 9, "tokens_in": 5345, "tokens_out": 1118, "duration_ms": 81493, "llm_duration_ms": 81473, "tool_duration_ms": 61052, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the study.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 2/10 — The answer correctly states low confidence, but it is not informative.\nCognitive Load: 5/10 — The answer is easy to follow but lacks content.\nOVERALL: 1/10 — The response fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly states the findings of the study.\nCitation Quality: 5/10 — The citation is indicated but not properly formatted or verified.\nCoverage: 5/10 — The answer provides the main findings but lacks depth or additional perspectives.\nCalibration: 8/10 — The confidence is high and justified by the accurate summary.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — The response accurately summarizes the study but lacks citation details and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the study's findings.\nCitation Quality: 5/10 — The citation is indicated but not properly formatted or verified.\nCoverage: 6/10 — The answer includes a bit more context but still lacks depth.\nCalibration: 8/10 — The confidence is high and justified by the accurate summary.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 7/10 — The response is accurate and slightly more informative but still lacks citation details.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the study's findings.\nCitation Quality: 4/10 — The citation is mentioned but lacks a source URL for verification.\nCoverage: 6/10 — The answer includes caveats but lacks detailed perspectives.\nCalibration: 8/10 — The confidence is high and justified by the accurate summary.\nCognitive Load: 8/10 — The answer is clear and well-structured.\nOVERALL: 7/10 — The response is accurate and includes caveats but lacks citation details.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 5 | 5 | 8 | 8 | 7 |\n| B2 | 10 | 5 | 6 | 8 | 8 | 7 |\n| MA | 10 | 4 | 6 | 8 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the study.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The low confidence is appropriate given the lack of information, but it still fails to provide any useful content.\nCognitive Load: 5/10 — The answer is easy to follow but lacks substance.\nOVERALL: 1/10 — The response fails to address the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 10/10 — The answer correctly states the findings of the study.\nCitation Quality: 5/10 — The citation is indicated but not properly formatted or verified.\nCoverage: 3/10 — The answer does not explore multiple perspectives or counterarguments.\nCalibration: 8/10 — The confidence is high and justified by the accurate information provided.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 7/10 — The answer is accurate and clear but lacks depth and proper citation.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the study's findings.\nCitation Quality: 5/10 — The citation is indicated but not properly formatted or verified.\nCoverage: 4/10 — The answer briefly mentions adversarial prefill attacks but lacks depth.\nCalibration: 8/10 — The high confidence is justified by the accurate information.\nCognitive Load: 8/10 — The answer is clear and easy to understand.\nOVERALL: 7/10 — The response is accurate and well-structured but lacks comprehensive coverage and proper citation.\n\n### MA: Multi-Agent\nAccuracy: 10/10 — The answer accurately reflects the study's findings.\nCitation Quality: 4/10 — The citation is mentioned but lacks a source URL for verification.\nCoverage: 5/10 — The answer includes caveats but lacks detailed exploration of the study's methodology.\nCalibration: 8/10 — The high confidence is justified by the accurate information.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 7/10 — The response is accurate and clear but lacks detailed coverage and proper citation.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 10 | 5 | 3 | 8 | 8 | 7 |\n| B2 | 10 | 5 | 4 | 8 | 8 | 7 |\n| MA | 10 | 4 | 5 | 8 | 8 | 7 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 4, "Coverage": 6, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 3, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 5, "Coverage": 4, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 }, "MA: Multi-Agent": { "Accuracy": 10, "Citation Quality": 4, "Coverage": 5, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } } }, "gpt-4o_q16": { "id": "fresh-20260623-016", "question": "What architectural default does 'Tapered Language Models' question, and what asymmetry motivates the question?", "answer_key": "It questions the default stack of identical layers with parameters allocated uniformly across depth. The motivation is evidence that layers contribute non-uniformly, with later layers refining rather than transforming the residual stream.", "source": "https://arxiv.org/abs/2606.23670v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The concept of \"Tapered Language Models\" questions the architectural default of using a uniform width across all layers in transformer-based language models. Typically, models like BERT (Devlin et al.", "llm_calls": 1, "tool_calls": 0, "tokens_in": 108, "tokens_out": 323, "duration_ms": 4518, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The architectural default that 'Tapered Language Models' question is the use of a stack of identical layers in which parameters are allocated uniformly across depth. This default is inherited from the", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1333, "tokens_out": 155, "duration_ms": 3659, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The architectural default that \"Tapered Language Models\" question is the uniform allocation of parameters across the depth of a model, a design inherited from the original transformer architecture. Th", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4459, "tokens_out": 657, "duration_ms": 9718, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe architectural default that 'Tapered Language Models' question is the use of a stack of identical layers where parameters are allocated uniformly across depth. The asymmetry that motivat", "llm_calls": 6, "tool_calls": 9, "tokens_in": 3863, "tokens_out": 1002, "duration_ms": 88919, "llm_duration_ms": 88901, "tool_duration_ms": 75730, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer incorrectly focuses on uniform width rather than uniform parameter allocation across depth.\nCitation Quality: 2/10 — Citations are real but not directly relevant to the specific concept of \"Tapered Language Models.\"\nCoverage: 4/10 — It discusses some aspects of layer capacity but misses the specific asymmetry of layer contributions.\nCalibration: 3/10 — The confidence is medium, which is appropriate given the inaccuracies, but the explanation is misleading.\nCognitive Load: 6/10 — The answer is relatively easy to follow but contains irrelevant information.\nOVERALL: 4/10 — The answer provides some context but fails to accurately address the specific architectural default and asymmetry.\n\n### B1: LLM + Search\nAccuracy: 9/10 — Correctly identifies the architectural default and asymmetry.\nCitation Quality: 8/10 — The citation is relevant and supports the claims, though not directly linked.\nCoverage: 7/10 — Covers the main points but lacks depth in discussing counterarguments or alternative perspectives.\nCalibration: 9/10 — High confidence is justified given the accuracy and relevance of the information.\nCognitive Load: 8/10 — The answer is concise and easy to follow.\nOVERALL: 8/10 — A strong answer that accurately addresses the question with relevant citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — Accurately identifies the architectural default and asymmetry, with additional insights.\nCitation Quality: 9/10 — Provides a relevant citation that directly supports the claims.\nCoverage: 9/10 — Offers a comprehensive view, including evidence and implications of the architectural change.\nCalibration: 10/10 — High confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — Well-structured and easy to understand.\nOVERALL: 9/10 — An excellent answer that thoroughly addresses the question with strong support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — Correctly identifies the architectural default and asymmetry.\nCitation Quality: 6/10 — The citation is relevant but lacks a direct URL, limiting verification.\nCoverage: 7/10 — Covers the main points but does not explore alternative perspectives or implications.\nCalibration: 8/10 — High confidence is mostly justified, though the lack of a direct citation link is a minor issue.\nCognitive Load: 8/10 — Clear and concise, but the lack of a direct citation link slightly detracts from clarity.\nOVERALL: 8/10 — A solid answer with minor citation issues but otherwise accurate and clear.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 2 | 4 | 3 | 6 | 4 |\n| B1 | 9 | 8 | 7 | 9 | 8 | 8 |\n| B2 | 10 | 9 | 9 | 10 | 9 | 9 |\n| MA | 9 | 6 | 7 | 8 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 5/10 — The answer incorrectly focuses on uniform width rather than uniform parameter allocation across depth.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 4/10 — It discusses the idea of non-uniform needs across layers but misses the specific asymmetry of refinement vs. transformation.\nCalibration: 4/10 — The confidence is medium, which somewhat matches the partial correctness of the answer.\nCognitive Load: 6/10 — The explanation is clear but slightly off-topic, which could confuse readers.\n\nOVERALL: 4/10 — The answer is partially correct but lacks focus on the specific architectural default and asymmetry.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer correctly identifies the architectural default and the asymmetry.\nCitation Quality: 7/10 — The citation is implied but not explicitly linked, reducing its effectiveness.\nCoverage: 8/10 — It covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified given the accuracy of the answer.\nCognitive Load: 8/10 — The explanation is concise and easy to follow.\n\nOVERALL: 8/10 — A strong answer with minor citation issues.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — The citation is relevant and supports the claims, though it could be more explicitly linked.\nCoverage: 9/10 — It provides additional insights into parameter allocation strategies.\nCalibration: 10/10 — The high confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — The explanation is clear and well-structured.\n\nOVERALL: 9/10 — An excellent answer with comprehensive coverage and strong citation.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer correctly identifies the architectural default and the asymmetry.\nCitation Quality: 6/10 — The citation is mentioned but lacks a specific URL, limiting its utility.\nCoverage: 8/10 — It covers the main points but lacks additional perspectives or counterarguments.\nCalibration: 9/10 — The high confidence is justified given the accuracy of the answer.\nCognitive Load: 8/10 — The explanation is clear and concise.\n\nOVERALL: 8/10 — A strong answer with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 5 | 0 | 4 | 4 | 6 | 4 |\n| B1 | 9 | 7 | 8 | 9 | 8 | 8 |\n| B2 | 10 | 9 | 9 | 10 | 9 | 9 |\n| MA | 9 | 6 | 8 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 2, "Coverage": 4, "Calibration": 3, "Cognitive Load": 6, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 5, "Citation Quality": 0, "Coverage": 4, "Calibration": 4, "Cognitive Load": 6, "OVERALL": 4 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 7, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 6, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q17": { "id": "fresh-20260623-017", "question": "How does 'On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners' model user-system interaction, and what conceptual decomposition does it introduce?", "answer_key": "It models user-system interaction as a bilevel cheap-talk game. It introduces a decomposition separating task inference from execution.", "source": "https://arxiv.org/abs/2606.23668v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "I cannot provide a detailed analysis of the paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" as I don't have access to specific papers or their contents. However", "llm_calls": 1, "tool_calls": 0, "tokens_in": 120, "tokens_out": 362, "duration_ms": 3643, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" models user-system interaction as a bilevel \"cheap-talk\" game. This approach analyzes how latent tasks are e", "llm_calls": 2, "tool_calls": 9, "tokens_in": 1510, "tokens_out": 217, "duration_ms": 2926, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" models user-system interaction as a bilevel \"cheap-talk\" game. This framework analyzes how latent tasks are ", "llm_calls": 4, "tool_calls": 9, "tokens_in": 4963, "tokens_out": 632, "duration_ms": 12045, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe paper \"On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners\" models user-system interaction as a bilevel \"cheap-talk\" game. This approach analyzes how latent ", "llm_calls": 6, "tool_calls": 9, "tokens_in": 4135, "tokens_out": 1221, "duration_ms": 80776, "llm_duration_ms": 80756, "tool_duration_ms": 63255, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately describe the paper's content or its key concepts.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 1/10 — The answer lacks multiple perspectives and counterarguments, focusing only on general practices.\nCalibration: 3/10 — The confidence is medium, which is slightly overstated given the lack of specific information.\nCognitive Load: 5/10 — The answer is somewhat easy to follow but lacks depth and specificity.\nOVERALL: 2/10 — The response is generic and does not address the specific question or paper.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the paper's modeling approach and conceptual decomposition.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 7/10 — The answer covers the main points but lacks discussion of counterarguments or alternative perspectives.\nCalibration: 9/10 — The confidence is high and justified by the accurate and well-supported content.\nCognitive Load: 8/10 — The answer is clear and well-structured, making it easy to follow.\nOVERALL: 8/10 — A strong response that accurately and clearly addresses the question with good citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the paper's modeling approach and conceptual decomposition.\nCitation Quality: 9/10 — The citation is appropriate and supports the claims effectively.\nCoverage: 7/10 — The answer provides a solid overview but lacks exploration of alternative viewpoints.\nCalibration: 9/10 — The high confidence is appropriate given the accurate and well-cited content.\nCognitive Load: 8/10 — The response is well-organized and easy to understand.\nOVERALL: 8/10 — A comprehensive and accurate answer with strong citation support and clarity.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the paper's content and key concepts.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims.\nCoverage: 7/10 — The answer is thorough but does not explore counterarguments or alternative interpretations.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported content.\nCognitive Load: 8/10 — The answer is clear and logically structured, making it easy to follow.\nOVERALL: 8/10 — A well-rounded response that accurately addresses the question with good citation support.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 1 | 3 | 5 | 2 |\n| B1 | 9 | 9 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 7 | 9 | 8 | 8 |\n| MA | 9 | 9 | 7 | 9 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 2/10 — The answer does not accurately describe the paper's content or its key concepts.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 1/10 — The answer lacks depth and does not address multiple perspectives or counterarguments.\nCalibration: 3/10 — The confidence is medium, which is slightly overstated given the lack of specific information.\nCognitive Load: 5/10 — The answer is somewhat clear but lacks structure and relevance to the question.\nOVERALL: 2/10 — The response is largely irrelevant and lacks specific information about the paper.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the paper's modeling of user-system interaction and conceptual decomposition.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 7/10 — The answer covers the main points but lacks discussion of counterarguments or alternative perspectives.\nCalibration: 9/10 — The confidence level is appropriate given the accuracy and citation quality.\nCognitive Load: 8/10 — The answer is clear and well-structured, though slightly dense.\nOVERALL: 8/10 — A strong response with accurate information and good citation quality.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer correctly identifies the key concepts from the paper.\nCitation Quality: 9/10 — The citation is accurate and supports the claims.\nCoverage: 7/10 — The answer provides a good overview but lacks exploration of alternative viewpoints.\nCalibration: 9/10 — The confidence is well-matched to the quality of the answer.\nCognitive Load: 8/10 — The response is clear and logically organized.\nOVERALL: 8/10 — A well-rounded answer with accurate information and strong citation support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately reflects the paper's content.\nCitation Quality: 9/10 — The citation is relevant and correctly supports the claims.\nCoverage: 6/10 — The answer is comprehensive but does not explore counterarguments or alternative interpretations.\nCalibration: 9/10 — The confidence is appropriate given the accuracy and citation quality.\nCognitive Load: 8/10 — The answer is clear and well-structured, though it could be more concise.\nOVERALL: 8/10 — A solid response with accurate information and good citation quality.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 2 | 0 | 1 | 3 | 5 | 2 |\n| B1 | 9 | 9 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 7 | 9 | 8 | 8 |\n| MA | 9 | 9 | 6 | 9 | 8 | 8 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 1, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 1, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 6, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } } }, "gpt-4o_q18": { "id": "fresh-20260623-018", "question": "What does MAS-PromptBench study, and why are system prompts described as an accessible optimization surface in multi-agent systems?", "answer_key": "It studies when prompt optimization improves multi-agent LLM systems. System prompts are accessible because they specify agents' roles/behaviors and can improve the system without model fine-tuning.", "source": "https://arxiv.org/abs/2606.23664v1", "answers": { "B0: Vanilla LLM": { "answer_preview": "The MAS-PromptBench study is not something I have specific information on, as it appears to be a specialized or recent topic that might not be covered in my training data up to October 2023. However, ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 111, "tokens_out": 359, "duration_ms": 4904, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "MAS-PromptBench studies the optimization of system prompts in multi-agent systems (MAS) that utilize large language models (LLMs). The study focuses on how prompt optimization can improve the performa", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1429, "tokens_out": 218, "duration_ms": 4089, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "MAS-PromptBench studies the optimization of system prompts in multi-agent systems (MAS) that consist of multiple large language model (LLM)-based agents. Each agent is assigned a system prompt and a p", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4828, "tokens_out": 703, "duration_ms": 10003, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nMAS-PromptBench studies the optimization of system prompts within multi-agent systems (MAS) that use large language models (LLMs). The research focuses on understanding when and how prompt ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3900, "tokens_out": 1141, "duration_ms": 71016, "llm_duration_ms": 71002, "tool_duration_ms": 53440, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 3/10 — The answer provides a general understanding of prompts but lacks specific details about MAS-PromptBench.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer touches on general concepts but does not address multiple perspectives or counterarguments.\nCalibration: 4/10 — The confidence level is medium, which is appropriate given the lack of specific information.\nCognitive Load: 5/10 — The answer is relatively easy to follow but lacks depth and specificity.\nOVERALL: 3/10 — The response is too general and lacks specific information about the study.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer accurately describes the study's focus and the concept of system prompts.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 7/10 — The answer addresses the challenges of prompt optimization but lacks counterarguments.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported information.\nCognitive Load: 8/10 — The answer is clear and concise, making it easy to follow.\nOVERALL: 8/10 — A well-rounded answer with accurate information and good citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer provides a detailed and accurate description of the study and its focus.\nCitation Quality: 9/10 — The citation is relevant and directly supports the claims.\nCoverage: 8/10 — The answer covers the study's challenges and objectives but could include more counterarguments.\nCalibration: 9/10 — The high confidence is appropriate given the detailed and accurate information.\nCognitive Load: 9/10 — The answer is well-structured and easy to understand.\nOVERALL: 9/10 — A comprehensive and well-supported answer with excellent clarity.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the study and the concept of system prompts.\nCitation Quality: 8/10 — The citation is relevant, but the source URL is incomplete.\nCoverage: 7/10 — The answer covers the main points but lacks counterarguments and specific examples.\nCalibration: 9/10 — The high confidence is justified by the accurate and clear explanation.\nCognitive Load: 8/10 — The answer is clear and concise, though it assumes some prior knowledge.\nOVERALL: 8/10 — A clear and accurate answer with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 3 | 0 | 2 | 4 | 5 | 3 |\n| B1 | 8 | 9 | 7 | 9 | 8 | 8 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 9 | 8 | 7 | 9 | 8 | 8 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 4/10 — The answer provides a general understanding of system prompts but lacks specific information about MAS-PromptBench.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 3/10 — The answer discusses general concepts but does not address multiple perspectives or counterarguments.\nCalibration: 5/10 — The confidence level is appropriately medium given the lack of specific information.\nCognitive Load: 6/10 — The answer is relatively easy to follow but lacks depth and specificity.\nOVERALL: 3/10 — The answer is vague and lacks direct relevance to the question.\n\n### B1: LLM + Search\nAccuracy: 9/10 — The answer accurately describes the study's focus and the concept of system prompts.\nCitation Quality: 9/10 — The citation is relevant and supports the claims made.\nCoverage: 8/10 — The answer addresses the challenges and benefits of prompt optimization in MAS.\nCalibration: 9/10 — The high confidence is justified by the accurate and well-supported information.\nCognitive Load: 8/10 — The answer is clear and well-structured, though it could be more detailed.\nOVERALL: 9/10 — A well-rounded and accurate answer with strong citation support.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer provides a precise and comprehensive explanation of the study.\nCitation Quality: 10/10 — The citation is accurate and directly supports the claims.\nCoverage: 9/10 — The answer covers the study's focus, challenges, and implications thoroughly.\nCalibration: 10/10 — The high confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — The answer is detailed yet easy to follow, though it assumes some prior knowledge.\nOVERALL: 10/10 — An exemplary answer that fully addresses the question with excellent support.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer accurately describes the study and the concept of system prompts.\nCitation Quality: 8/10 — The citation is relevant, but the source URL is incomplete.\nCoverage: 8/10 — The answer discusses the study's focus and implications but lacks specific examples.\nCalibration: 9/10 — The high confidence is appropriate given the accuracy of the information.\nCognitive Load: 8/10 — The answer is clear and concise but could be more detailed for broader understanding.\nOVERALL: 9/10 — A strong answer with minor citation and detail issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 4 | 0 | 3 | 5 | 6 | 3 |\n| B1 | 9 | 9 | 8 | 9 | 8 | 9 |\n| B2 | 10 | 10 | 9 | 10 | 9 | 10 |\n| MA | 9 | 8 | 8 | 9 | 8 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 3, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 9, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 7, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 8 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 4, "Citation Quality": 0, "Coverage": 3, "Calibration": 5, "Cognitive Load": 6, "OVERALL": 3 }, "B1: LLM + Search": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 10, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 10 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 8, "OVERALL": 9 } } }, "gpt-4o_q19": { "id": "fresh-20260623-019", "question": "In Google's June 22, 2026 Jules post, what gap in SWE-Bench-style evaluation is identified, and what is 'insight policy'?", "answer_key": "SWE-Bench evaluates task completion for narrowly defined bugs, but not open-ended goals for proactive agents. Insight policy is the ability to decide what matters, what evidence supports it, and whether to interrupt the developer or stay silent.", "source": "https://developers.googleblog.com/measuring-what-matters-with-jules/", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm sorry, but I cannot provide details about Google's June 22, 2026 Jules post or any specific content from it, as my training only includes data up to October 2023. Therefore, I do not have access t", "llm_calls": 1, "tool_calls": 0, "tokens_in": 119, "tokens_out": 129, "duration_ms": 2251, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "In Google's June 22, 2026 Jules post, a gap identified in SWE-Bench-style evaluation is the lack of benchmarks for goals. While public benchmarks like SWE-Bench test an agent’s ability to complete nar", "llm_calls": 2, "tool_calls": 8, "tokens_in": 1164, "tokens_out": 195, "duration_ms": 4454, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "In Google's June 22, 2026 Jules post, the identified gap in SWE-Bench-style evaluation is the absence of benchmarks for evaluating an agent's ability to achieve higher-level goals. While existing benc", "llm_calls": 4, "tool_calls": 8, "tokens_in": 4853, "tokens_out": 672, "duration_ms": 17459, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe gap identified in SWE-Bench-style evaluation is the lack of benchmarks for evaluating agents on broader goals rather than narrowly defined tasks. SWE-Bench currently focuses on testing ", "llm_calls": 6, "tool_calls": 8, "tokens_in": 3809, "tokens_out": 1091, "duration_ms": 65064, "llm_duration_ms": 65046, "tool_duration_ms": 49063, "converged": true, "iterations": 1 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the post.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 5/10 — The confidence is high, but the answer admits its limitations.\nCognitive Load: 5/10 — The answer is clear but lacks content.\nOVERALL: 2/10 — The response is clear about its limitations but provides no useful information.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the gap and explains 'insight policy.'\nCitation Quality: 0/10 — Citations are mentioned but not provided.\nCoverage: 7/10 — The answer covers the main points but lacks depth in counterarguments.\nCalibration: 7/10 — The confidence is high and mostly justified by the content.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 6/10 — A mostly accurate and clear answer, but lacks proper citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 9/10 — The answer aligns well with the authoritative answer key.\nCitation Quality: 9/10 — A relevant citation is provided.\nCoverage: 8/10 — The answer covers multiple aspects but could include more counterarguments.\nCalibration: 9/10 — The confidence is high and well-supported by the content.\nCognitive Load: 9/10 — The answer is clear and logically structured.\nOVERALL: 9/10 — A comprehensive and well-cited answer that closely matches the authoritative key.\n\n### MA: Multi-Agent\nAccuracy: 8/10 — The answer correctly identifies the gap and explains 'insight policy.'\nCitation Quality: 5/10 — Citations are mentioned but not properly linked.\nCoverage: 8/10 — The answer covers the main points but lacks depth in counterarguments.\nCalibration: 8/10 — The confidence is high and mostly justified by the content.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 7/10 — A mostly accurate and clear answer, but lacks proper citation linkage.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 8 | 0 | 7 | 7 | 8 | 6 |\n| B2 | 9 | 9 | 8 | 9 | 9 | 9 |\n| MA | 8 | 5 | 8 | 8 | 8 | 7 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any factual information about the post.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address any perspectives or counterarguments.\nCalibration: 2/10 — The confidence is high, but the answer lacks any factual content.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 1/10 — The response fails to address the question due to data limitations.\n\n### B1: LLM + Search\nAccuracy: 8/10 — The answer correctly identifies the gap and explains 'insight policy.'\nCitation Quality: 0/10 — Citations are indicated but not provided.\nCoverage: 7/10 — The answer covers the main points but lacks depth in counterarguments.\nCalibration: 8/10 — The confidence is appropriate given the accuracy of the content.\nCognitive Load: 8/10 — The answer is well-structured and easy to follow.\nOVERALL: 6/10 — A mostly accurate and clear response, but lacks proper citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 10/10 — The answer accurately reflects the authoritative answer key.\nCitation Quality: 9/10 — A real and relevant citation is provided.\nCoverage: 9/10 — The answer covers multiple aspects of the topic.\nCalibration: 10/10 — The confidence is well-matched to the quality of the answer.\nCognitive Load: 9/10 — The answer is clear and logically structured.\nOVERALL: 9/10 — A comprehensive and well-cited response.\n\n### MA: Multi-Agent\nAccuracy: 9/10 — The answer is mostly accurate but slightly less detailed than B2.\nCitation Quality: 8/10 — Citations are mentioned but not fully provided.\nCoverage: 8/10 — The answer covers the main points but could explore more perspectives.\nCalibration: 9/10 — The confidence is appropriate for the quality of the answer.\nCognitive Load: 9/10 — The answer is clear and easy to understand.\nOVERALL: 9/10 — A well-rounded and mostly accurate response, with minor citation issues.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 2 | 5 | 1 |\n| B1 | 8 | 0 | 7 | 8 | 8 | 6 |\n| B2 | 10 | 9 | 9 | 10 | 9 | 9 |\n| MA | 9 | 8 | 8 | 9 | 9 | 9 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 0, "Coverage": 7, "Calibration": 7, "Cognitive Load": 8, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 9, "Citation Quality": 9, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 8, "Citation Quality": 5, "Coverage": 8, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 7 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 2, "Cognitive Load": 5, "OVERALL": 1 }, "B1: LLM + Search": { "Accuracy": 8, "Citation Quality": 0, "Coverage": 7, "Calibration": 8, "Cognitive Load": 8, "OVERALL": 6 }, "B2: LLM + Search + Reflection": { "Accuracy": 10, "Citation Quality": 9, "Coverage": 9, "Calibration": 10, "Cognitive Load": 9, "OVERALL": 9 }, "MA: Multi-Agent": { "Accuracy": 9, "Citation Quality": 8, "Coverage": 8, "Calibration": 9, "Cognitive Load": 9, "OVERALL": 9 } } }, "gpt-4o_q20": { "id": "fresh-20260623-020", "question": "From the LangChain GitHub release feed around June 22-23, 2026, which four package release tags appeared most recently?", "answer_key": "langchain-openrouter==0.2.4, langchain-openai==1.3.3, langchain-anthropic==1.4.7, and langchain==1.3.11.", "source": "https://github.com/langchain-ai/langchain/releases.atom", "answers": { "B0: Vanilla LLM": { "answer_preview": "I'm unable to provide information on specific GitHub release tags from June 22-23, 2026, as my training only includes data up to October 2023. Therefore, I cannot access or verify future or real-time ", "llm_calls": 1, "tool_calls": 0, "tokens_in": 115, "tokens_out": 139, "duration_ms": 2147, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B1: LLM + Search": { "answer_preview": "The four most recent package release tags from the LangChain GitHub release feed around June 22-23, 2026, are:\n\n1. langchain-openrouter==0.2.4\n2. FIELD_MISSING\n3. FIELD_MISSING\n4. FIELD_MISSING\n\nConfi", "llm_calls": 2, "tool_calls": 10, "tokens_in": 1563, "tokens_out": 151, "duration_ms": 2667, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "B2: LLM + Search + Reflection": { "answer_preview": "The four most recent package release tags from the LangChain GitHub release feed around June 22-23, 2026, are:\n\n1. langchain-openrouter==0.2.4\n2. FIELD_MISSING\n3. FIELD_MISSING\n4. FIELD_MISSING\n\nConfi", "llm_calls": 4, "tool_calls": 10, "tokens_in": 5085, "tokens_out": 567, "duration_ms": 11081, "llm_duration_ms": null, "tool_duration_ms": null, "converged": null, "iterations": null }, "MA: Multi-Agent": { "answer_preview": "## Answer\n\nThe four most recent package release tags from the LangChain GitHub release feed around June 22-23, 2026, are:\n1. langchain-openrouter==0.2.4\n2. FIELD_MISSING\n3. FIELD_MISSING\n4. FIELD_MISS", "llm_calls": 14, "tool_calls": 10, "tokens_in": 12388, "tokens_out": 1934, "duration_ms": 102279, "llm_duration_ms": 102245, "tool_duration_ms": 77848, "converged": false, "iterations": 3 } }, "self_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any of the required release tags.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but lacks substance.\nOVERALL: 2/10 — The response is clear but fails to answer the question or provide any useful information.\n\n### B1: LLM + Search\nAccuracy: 2/10 — Only one correct release tag is provided.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer acknowledges missing information but does not explore other perspectives.\nCalibration: 4/10 — Confidence is medium despite missing most of the required information.\nCognitive Load: 5/10 — The answer is straightforward but incomplete.\nOVERALL: 3/10 — The answer is partially correct but lacks depth and citations.\n\n### B2: LLM + Search + Reflection\nAccuracy: 2/10 — Only one correct release tag is provided.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer acknowledges missing information but does not explore other perspectives.\nCalibration: 4/10 — Confidence is medium despite missing most of the required information.\nCognitive Load: 5/10 — The answer is straightforward but incomplete.\nOVERALL: 3/10 — The answer is partially correct but lacks depth and citations.\n\n### MA: Multi-Agent\nAccuracy: 2/10 — Only one correct release tag is provided.\nCitation Quality: 1/10 — A citation is mentioned but not properly linked or detailed.\nCoverage: 2/10 — The answer acknowledges missing information but does not explore other perspectives.\nCalibration: 3/10 — Confidence is low, which is appropriate given the incomplete information.\nCognitive Load: 5/10 — The answer is straightforward but incomplete.\nOVERALL: 3/10 — The answer is partially correct but lacks depth and proper citations.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 2 | 0 | 2 | 4 | 5 | 3 |\n| B2 | 2 | 0 | 2 | 4 | 5 | 3 |\n| MA | 2 | 1 | 2 | 3 | 5 | 3 |", "unified_grade": "### B0: Vanilla LLM\nAccuracy: 0/10 — The answer does not provide any of the required release tags.\nCitation Quality: 0/10 — No citations are provided.\nCoverage: 0/10 — The answer does not address the question or provide any perspectives.\nCalibration: 5/10 — The confidence is appropriately low given the lack of information.\nCognitive Load: 5/10 — The answer is clear but uninformative.\nOVERALL: 2/10 — The answer fails to address the question but is clear about its limitations.\n\n### B1: LLM + Search\nAccuracy: 2/10 — Only one correct release tag is provided.\nCitation Quality: 0/10 — No citations are provided to support the claims.\nCoverage: 2/10 — The answer acknowledges missing information but does not provide multiple perspectives.\nCalibration: 4/10 — The confidence is medium despite the incomplete answer.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth.\nOVERALL: 3/10 — The answer is partially correct but lacks citations and depth.\n\n### B2: LLM + Search + Reflection\nAccuracy: 2/10 — Only one correct release tag is provided.\nCitation Quality: 1/10 — A vague reference to a source is made but not properly cited.\nCoverage: 2/10 — The answer acknowledges missing information but does not provide multiple perspectives.\nCalibration: 4/10 — The confidence is medium despite the incomplete answer.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth.\nOVERALL: 3/10 — The answer is partially correct but lacks proper citations and depth.\n\n### MA: Multi-Agent\nAccuracy: 2/10 — Only one correct release tag is provided.\nCitation Quality: 1/10 — A vague reference to a source is made but not properly cited.\nCoverage: 2/10 — The answer acknowledges missing information but does not provide multiple perspectives.\nCalibration: 3/10 — The confidence is low, which is appropriate given the incomplete answer.\nCognitive Load: 5/10 — The answer is easy to follow but lacks depth.\nOVERALL: 3/10 — The answer is partially correct but lacks proper citations and depth.\n\n| Approach | Accuracy | Citations | Coverage | Calibration | Cognitive Load | Overall |\n|----------|----------|-----------|----------|-------------|----------------|---------|\n| B0 | 0 | 0 | 0 | 5 | 5 | 2 |\n| B1 | 2 | 0 | 2 | 4 | 5 | 3 |\n| B2 | 2 | 1 | 2 | 4 | 5 | 3 |\n| MA | 2 | 1 | 2 | 3 | 5 | 3 |", "self_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B2: LLM + Search + Reflection": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "MA: Multi-Agent": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 } }, "unified_grade_scores": { "B0: Vanilla LLM": { "Accuracy": 0, "Citation Quality": 0, "Coverage": 0, "Calibration": 5, "Cognitive Load": 5, "OVERALL": 2 }, "B1: LLM + Search": { "Accuracy": 2, "Citation Quality": 0, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "B2: LLM + Search + Reflection": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 2, "Calibration": 4, "Cognitive Load": 5, "OVERALL": 3 }, "MA: Multi-Agent": { "Accuracy": 2, "Citation Quality": 1, "Coverage": 2, "Calibration": 3, "Cognitive Load": 5, "OVERALL": 3 } } } } }